Release Falcon-H1 Rust CUDA implementation

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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Cargo.toml

@@ -0,0 +1,23 @@
+[package]
+name = "falcon_h1_rust"
+version = "0.2.0"
+edition = "2021"
+license = "MIT"
+description = "Falcon-H1 Mamba-2 + attention CPU/CUDA inference HTTP API"
+
+[features]
+default = []
+candle = ["dep:candle-core", "dep:candle-nn"]
+cuda = ["candle", "candle-core/cuda", "candle-nn/cuda"]
+
+[dependencies]
+candle-core = { version = "=0.11.1", optional = true }
+candle-nn = { version = "=0.11.1", optional = true }
+
+[lib]
+name = "falcon_h1_rust"
+path = "src/lib.rs"
+
+[[bin]]
+name = "falcon-h1-api"
+path = "src/main.rs"

LICENSE

@@ -0,0 +1,11 @@
+Falcon-H1-0.5B-Rust
+
+The pretrained weights originate from tiiuae/Falcon-H1-0.5B-Instruct.
+
+They remain subject to the Falcon-LLM License.
+Official terms:
+https://falconllm.tii.ae/falcon-terms-and-conditions.html
+
+Falcon-H1-0.5B-Rust is built using Falcon LLM technology from the Technology Innovation Institute.
+
+This notice does not replace the full license terms.

README.md

@@ -0,0 +1,93 @@
+---
+license: other
+license_name: falcon-llm-license
+license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html
+base_model: tiiuae/Falcon-H1-0.5B-Instruct
+tags:
+- rust
+- cuda
+- candle
+- mamba2
+- falcon-h1
+- text-generation
+---
+
+# Falcon-H1-0.5B — Rust CUDA Transformer
+
+A Rust implementation of the pretrained Falcon-H1-0.5B-Instruct model.
+
+## Architecture
+
+The implementation reproduces Falcon-H1's hybrid architecture:
+
+- Parallel Mamba-2 and attention
+- Grouped-query attention
+- Rotary positional embeddings
+- RMSNorm
+- SwiGLU feed-forward networks
+- Original model dimensions and parameters
+
+Implemented using Rust and Hugging Face Candle.
+
+## Pretrained Weights
+
+The checkpoint contains converted FP32 weights from:
+
+https://huggingface.co/tiiuae/Falcon-H1-0.5B-Instruct
+
+No additional training or fine-tuning was performed.
+
+The checkpoint uses a custom .f32pack format.
+It is not directly loadable using Transformers.from_pretrained.
+
+## GPU Validation
+
+Tested on NVIDIA Tesla T4 using Google Colab.
+
+Compared against the original Hugging Face implementation.
+
+- Maximum absolute logit error: 7.0522e-5
+- Mean absolute logit error: 1.4631e-5
+- Identical top-5 token predictions
+
+Validation used the three-token input [1, 2, 3].
+
+## Build
+
+Install Rust and CUDA, then execute:
+
+    cargo build --release --features cuda
+
+## Start HTTP API
+
+    ./target/release/falcon-h1-api --checkpoint falcon-h1.f32pack --device cuda
+
+## API Endpoints
+
+- GET /health
+- GET /v1/model
+- POST /v1/logits
+- POST /v1/generate
+
+The current API accepts token IDs.
+
+## CPU Support
+
+The architecture also supports CPU execution.
+
+## Attribution
+
+Original pretrained model: Technology Innovation Institute (TII).
+
+Original model weights are governed by the Falcon-LLM License.
+
+See the original model repository for licensing conditions.
+
+## Implementation Status
+
+- Rust implementation: completed
+- CUDA inference: validated on T4
+- Numerical agreement: verified for a three-token input
+- HTTP inference API: implemented
+- Knowledge distillation: not yet implemented
+- GPU training optimization: pending

chat_template.jinja

@@ -0,0 +1,17 @@
+{{bos_token}}
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "You are a function calling AI model. You are provided with function signature within <tools> </tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions.\n<tools>\n" }}
+    {%- for tool in tools %}[{{- tool | tojson }}]{%- endfor %}
+    {{- "\n</tools>\nFor each function call, return a json object with function name and arguments within <tool_call> </tool_call> tags with the following schema:\n<tool_call>\n{'arguments': <args-dict>, 'name': <function-name>}\n</tool_call>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}{% for message in messages %}{%- if message.role != 'system' %}{{'<|im_start|>' + message['role'] + '
+' + message['content'] + '<|im_end|>' + '
+'}}{%- endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
+' }}{% endif %}

config.json

@@ -0,0 +1,64 @@
+{
+  "architectures": [
+    "FalconH1ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "attention_in_multiplier": 1.0,
+  "attention_out_multiplier": 0.9375,
+  "attn_layer_indices": null,
+  "bos_token_id": 1,
+  "embedding_multiplier": 5.656854249492381,
+  "eos_token_id": 11,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 2048,
+  "key_multiplier": 0.39062499999999994,
+  "lm_head_multiplier": 0.0390625,
+  "mamba_chunk_size": 128,
+  "mamba_conv_bias": true,
+  "mamba_d_conv": 4,
+  "mamba_d_head": 64,
+  "mamba_d_ssm": 1536,
+  "mamba_d_state": 128,
+  "mamba_expand": 2,
+  "mamba_n_groups": 1,
+  "mamba_n_heads": 24,
+  "mamba_norm_before_gate": false,
+  "mamba_proj_bias": false,
+  "mamba_rms_norm": false,
+  "mamba_use_mlp": true,
+  "max_position_embeddings": 16384,
+  "mlp_bias": false,
+  "mlp_expansion_factor": 8,
+  "mlp_multipliers": [
+    0.8838834764831844,
+    0.5859375
+  ],
+  "model_type": "falcon_h1",
+  "num_attention_heads": 8,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 2,
+  "num_logits_to_keep": 1,
+  "pad_token_id": 0,
+  "projectors_bias": false,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 100000000000.0,
+  "ssm_in_multiplier": 1.25,
+  "ssm_multipliers": [
+    0.3535533905932738,
+    0.25,
+    0.3535533905932738,
+    0.5,
+    0.3535533905932738
+  ],
+  "ssm_out_multiplier": 0.23570226039551587,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.52.0.dev0",
+  "use_cache": true,
+  "vocab_size": 32784
+}

falcon-h1.f32pack

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0c349f92fcb98961241788891f15b7d30d7fadc85bf2de818804ca9b41eb6d14
+size 2085671072

generation_config.json

@@ -0,0 +1,7 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": [11, 228],
+  "pad_token_id": 0,
+  "transformers_version": "4.52.0.dev0"
+}

special_tokens_map.json

@@ -0,0 +1,356 @@
+{
+  "additional_special_tokens": [
+    "<|pad|>",
+    ">>ABSTRACT<<",
+    ">>INTRODUCTION<<",
+    ">>SUMMARY<<",
+    ">>COMMENT<<",
+    ">>ANSWER<<",
+    ">>QUESTION<<",
+    ">>DOMAIN<<",
+    ">>PREFIX<<",
+    ">>SUFFIX<<",
+    ">>MIDDLE<<",
+    "<|finetune_right_pad_id|>",
+    "<|start_header_id|>",
+    "<|end_header_id|>",
+    "<|eom_id|>",
+    "<|eot_id|>",
+    "<|begin_of_text|>",
+    ">>TITLE<<",
+    "<tool_response>",
+    "</tool_response>",
+    "<tool_call>",
+    "</tool_call>",
+    "<schema>",
+    "</schema>",
+    "<scratch_pad>",
+    "</scratch_pad>",
+    "<thinking>",
+    "</thinking>",
+    "<explanation>",
+    "</explanation>",
+    "<file_sep>",
+    "<repo_name>",
+    "<|im_end|>",
+    "<|im_start|>",
+    ">>UNUSED_119<<",
+    ">>UNUSED_120<<",
+    "<|system|>",
+    ">>UNUSED_218<<",
+    ">>UNUSED_219<<",
+    ">>UNUSED_220<<",
+    ">>UNUSED_221<<",
+    ">>UNUSED_222<<",
+    ">>UNUSED_223<<",
+    ">>UNUSED_224<<",
+    ">>UNUSED_225<<",
+    ">>UNUSED_226<<",
+    ">>UNUSED_227<<",
+    ">>UNUSED_228<<",
+    ">>UNUSED_229<<",
+    ">>UNUSED_230<<",
+    ">>UNUSED_231<<",
+    ">>UNUSED_232<<",
+    ">>UNUSED_233<<",
+    ">>UNUSED_234<<",
+    ">>UNUSED_235<<",
+    ">>UNUSED_236<<",
+    ">>UNUSED_237<<",
+    ">>UNUSED_238<<",
+    ">>UNUSED_239<<",
+    ">>UNUSED_240<<",
+    ">>UNUSED_241<<",
+    ">>UNUSED_242<<",
+    ">>UNUSED_243<<",
+    ">>UNUSED_244<<",
+    ">>UNUSED_245<<",
+    ">>UNUSED_246<<",
+    ">>UNUSED_247<<",
+    ">>UNUSED_248<<",
+    ">>UNUSED_249<<",
+    ">>UNUSED_250<<",
+    ">>UNUSED_251<<",
+    ">>UNUSED_252<<",
+    ">>UNUSED_253<<",
+    ">>UNUSED_254<<",
+    ">>UNUSED_255<<",
+    ">>UNUSED_256<<",
+    ">>UNUSED_257<<",
+    ">>UNUSED_258<<",
+    ">>UNUSED_259<<",
+    ">>UNUSED_260<<",
+    ">>UNUSED_261<<",
+    ">>UNUSED_262<<",
+    ">>UNUSED_263<<",
+    ">>UNUSED_264<<",
+    ">>UNUSED_265<<",
+    ">>UNUSED_266<<",
+    ">>UNUSED_267<<",
+    ">>UNUSED_268<<",
+    ">>UNUSED_269<<",
+    ">>UNUSED_270<<",
+    ">>UNUSED_271<<",
+    ">>UNUSED_272<<",
+    ">>UNUSED_273<<",
+    ">>UNUSED_274<<",
+    ">>UNUSED_275<<",
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+    ">>UNUSED_278<<",
+    ">>UNUSED_279<<",
+    ">>UNUSED_280<<",
+    ">>UNUSED_281<<",
+    ">>UNUSED_282<<",
+    ">>UNUSED_283<<",
+    ">>UNUSED_284<<",
+    ">>UNUSED_285<<",
+    ">>UNUSED_286<<",
+    ">>UNUSED_287<<",
+    ">>UNUSED_288<<",
+    ">>UNUSED_289<<",
+    ">>UNUSED_290<<",
+    ">>UNUSED_291<<",
+    ">>UNUSED_292<<",
+    ">>UNUSED_293<<",
+    ">>UNUSED_294<<",
+    ">>UNUSED_295<<",
+    ">>UNUSED_296<<",
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+    ">>UNUSED_299<<",
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+    ">>UNUSED_387<<",
+    ">>UNUSED_388<<",
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+    ">>UNUSED_392<<",
+    ">>UNUSED_393<<",
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+    ">>UNUSED_398<<",
+    ">>UNUSED_399<<",
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+    ">>UNUSED_435<<",
+    ">>UNUSED_436<<",
+    ">>UNUSED_437<<",
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+    ">>UNUSED_440<<",
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+    ">>UNUSED_442<<",
+    ">>UNUSED_443<<",
+    ">>UNUSED_444<<",
+    ">>UNUSED_445<<",
+    ">>UNUSED_446<<",
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+    ">>UNUSED_449<<",
+    ">>UNUSED_450<<",
+    ">>UNUSED_451<<",
+    ">>UNUSED_452<<",
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+    ">>UNUSED_454<<",
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+    ">>UNUSED_456<<",
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+    ">>UNUSED_458<<",
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+    ">>UNUSED_474<<",
+    ">>UNUSED_475<<",
+    ">>UNUSED_476<<",
+    ">>UNUSED_477<<",
+    ">>UNUSED_478<<",
+    ">>UNUSED_479<<",
+    ">>UNUSED_480<<",
+    ">>UNUSED_481<<",
+    ">>UNUSED_482<<",
+    ">>UNUSED_483<<",
+    ">>UNUSED_484<<",
+    ">>UNUSED_485<<",
+    ">>UNUSED_486<<",
+    ">>UNUSED_487<<",
+    ">>UNUSED_488<<",
+    ">>UNUSED_489<<",
+    ">>UNUSED_490<<",
+    ">>UNUSED_491<<",
+    ">>UNUSED_492<<",
+    ">>UNUSED_493<<",
+    ">>UNUSED_494<<",
+    ">>UNUSED_495<<",
+    ">>UNUSED_496<<",
+    ">>UNUSED_497<<",
+    ">>UNUSED_498<<",
+    ">>UNUSED_499<<",
+    ">>UNUSED_500<<",
+    ">>UNUSED_501<<",
+    ">>UNUSED_502<<",
+    ">>UNUSED_503<<",
+    ">>UNUSED_504<<",
+    ">>UNUSED_505<<",
+    ">>UNUSED_506<<",
+    ">>UNUSED_507<<",
+    ">>UNUSED_508<<",
+    ">>UNUSED_509<<",
+    ">>UNUSED_510<<",
+    ">>UNUSED_511<<"
+  ],
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|end_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

src/candle_backend.rs

@@ -0,0 +1,229 @@
+//! CUDA-capable Candle tensor implementation of Falcon-H1 inference.
+//! All projection, convolution, attention, and Mamba recurrence tensors remain
+//! on the selected device. Only input token IDs and final logits cross to CPU.
+//! The Mamba scan is a reference *sequential* scan (not fused SSD CUDA kernel).
+//! This backend is for INFERENCE; distillation/training is not implemented here.
+use std::collections::HashMap;
+use candle_core::{DType, Device, Result, Tensor as CTensor};
+use candle_nn::ops;
+use crate::{validate_weights, Config, Weights};
+
+pub struct CandleModel {
+    pub config: Config,
+    pub device: Device,
+    weights: HashMap<String, CTensor>,
+    group_index: CTensor,
+    mup: CTensor,
+}
+
+impl CandleModel {
+    pub fn new(config: Config, weights: Weights, device: Device) -> std::result::Result<Self, String> {
+        validate_weights(&config, &weights)?;
+        let mut mapped = HashMap::with_capacity(weights.len());
+        for (name, w) in weights {
+            let is_linear = w.shape.len()==2 && name.ends_with(".weight")
+                && name!="model.embed_tokens.weight";
+            let t = if is_linear {
+                let (rows,cols)=(w.shape[0],w.shape[1]);
+                let mut transposed=vec![0f32;rows*cols];
+                for row in 0..rows {for col in 0..cols {
+                    transposed[col*rows+row]=w.data[row*cols+col];
+                }}
+                CTensor::from_vec(transposed,(cols,rows),&device)
+            } else {
+                CTensor::from_vec(w.data,w.shape,&device)
+            }.map_err(|e|format!("{name}: {e}"))?;
+            mapped.insert(name, t);
+        }
+        let p = config.mamba_dim * 2 + 2 * config.groups * config.state + config.mamba_heads;
+        let parts = [config.mamba_dim, config.mamba_dim,
+            config.groups*config.state, config.groups*config.state, config.mamba_heads];
+        let mut scale = Vec::with_capacity(p);
+        for (len, factor) in parts.into_iter().zip(config.ssm_multipliers) {
+            scale.extend(std::iter::repeat(config.ssm_in_multiplier * factor).take(len));
+        }
+        let mup = CTensor::from_vec(scale, (1, p), &device).map_err(|e|e.to_string())?;
+        let ids = (0..config.mamba_heads)
+            .map(|h| (h/(config.mamba_heads/config.groups)) as u32).collect::<Vec<_>>();
+        let group_index= CTensor::from_vec(ids, config.mamba_heads, &device).map_err(|e|e.to_string())?;
+        Ok(Self { config, device, weights:mapped, group_index, mup })
+    }
+    pub fn device_name(&self) -> &'static str {
+        match self.device { Device::Cuda(_) => "cuda", Device::Cpu => "cpu-candle", Device::Metal(_) => "metal" }
+    }
+    fn w(&self, name: &str) -> &CTensor { &self.weights[name] }
+    fn linear(&self, x: &CTensor, name: &str) -> Result<CTensor> {
+        x.matmul(self.w(name))
+    }
+    fn norm(&self, x: &CTensor, name: &str) -> Result<CTensor> {
+        let denom = x.sqr()?.mean_keepdim(1)?.affine(1., self.config.eps as f64)?.sqrt()?;
+        x.broadcast_div(&denom)?.broadcast_mul(self.w(name))
+    }
+    fn rope(&self, input: &CTensor, n:usize, heads:usize, cos:&CTensor, sin:&CTensor)->Result<CTensor> {
+        let hd=self.config.head_dim;
+        let x=input.reshape((n,heads,hd))?;
+        let a=x.narrow(2,0,hd/2)?;
+        let b=x.narrow(2,hd/2,hd/2)?;
+        let real=a.broadcast_mul(cos)?.broadcast_sub(&b.broadcast_mul(sin)?)?;
+        let imag=b.broadcast_mul(cos)?.broadcast_add(&a.broadcast_mul(sin)?)?;
+        CTensor::cat(&[real,imag],2)
+    }
+    fn rope_tables(&self,n:usize)->Result<(CTensor,CTensor)>{
+        let hd=self.config.head_dim;
+        let mut cs=Vec::with_capacity(n*hd/2);
+        let mut sn=Vec::with_capacity(n*hd/2);
+        for t in 0..n { for j in 0..hd/2 {
+            let a=(t as f32)/self.config.rope_theta.powf((2*j) as f32/hd as f32);
+            let (s,c)=a.sin_cos();cs.push(c);sn.push(s);
+        }}
+        Ok((CTensor::from_vec(cs,(n,1,hd/2),&self.device)?,
+            CTensor::from_vec(sn,(n,1,hd/2),&self.device)?))
+    }
+    fn attention(&self, x:&CTensor, prefix:&str, n:usize,
+                 cos:&CTensor, sin:&CTensor, mask:&CTensor)->Result<CTensor>{
+        let c=&self.config;
+        let a=x.affine(c.attention_in_multiplier as f64,0.)?;
+        let q=self.linear(&a,&format!("{prefix}self_attn.q_proj.weight"))?;
+        let k=self.linear(&a,&format!("{prefix}self_attn.k_proj.weight"))?
+            .affine(c.key_multiplier as f64,0.)?;
+        let v=self.linear(&a,&format!("{prefix}self_attn.v_proj.weight"))?;
+        let q=self.rope(&q,n,c.heads,cos,sin)?;
+        let k=self.rope(&k,n,c.kv_heads,cos,sin)?;
+        let v=v.reshape((n,c.kv_heads,c.head_dim))?;
+        let mut heads=Vec::with_capacity(c.heads);
+        for h in 0..c.heads {
+            let kv=h/(c.heads/c.kv_heads);
+            let qi=q.narrow(1,h,1)?.squeeze(1)?.contiguous()?;
+            let ki=k.narrow(1,kv,1)?.squeeze(1)?.contiguous()?;
+            let vi=v.narrow(1,kv,1)?.squeeze(1)?.contiguous()?;
+            let scores=qi.matmul(&ki.t()?.contiguous()?)?
+                .affine(1.0/(c.head_dim as f64).sqrt(),0.)?
+                .broadcast_add(mask)?;
+            let probs=ops::softmax(&scores,1)?;
+            heads.push(probs.matmul(&vi)?);
+        }
+        // Each head is [time,head_dim]. Horizontal concat recovers Q projection width.
+        let joined=CTensor::cat(&heads,1)?;
+        self.linear(&joined,&format!("{prefix}self_attn.o_proj.weight"))?
+            .affine(c.attention_out_multiplier as f64,0.)
+    }
+    fn mamba(&self, x:&CTensor,prefix:&str,n:usize)->Result<CTensor>{
+        let c=&self.config;
+        let md=c.mamba_dim;
+        let cd=md+2*c.groups*c.state;
+        let hd=md/c.mamba_heads;
+        // All tokens projected at once; mup factors are applied to the correct
+        // [gate, X, B, C, dt] in_proj sections, as in Falcon-H1.
+        let proj=self.linear(x,&format!("{prefix}mamba.in_proj.weight"))?
+            .broadcast_mul(&self.mup)?;
+        let gate=proj.narrow(1,0,md)?;
+        let conv_in=proj.narrow(1,md,cd)?;
+        let dt=proj.narrow(1,md+cd,c.mamba_heads)?;
+        let conv_w=self.w(&format!("{prefix}mamba.conv1d.weight"))
+            .reshape((cd,c.conv))?;
+        let conv_bias=self.w(&format!("{prefix}mamba.conv1d.bias"));
+        // Depthwise causal Conv1d, vectorized across the entire time dimension.
+        let zeros=CTensor::zeros((c.conv-1,cd),DType::F32,&self.device)?;
+        let padded=CTensor::cat(&[zeros,conv_in],0)?;
+        let mut conv=CTensor::zeros((n,cd),DType::F32,&self.device)?;
+        for kernel in 0..c.conv {
+            let frame=padded.narrow(0,kernel,n)?;
+            let coeff=conv_w.narrow(1,kernel,1)?.transpose(0,1)?;
+            conv=conv.broadcast_add(&frame.broadcast_mul(&coeff)?)?;
+        }
+        let conv=ops::silu(&conv.broadcast_add(conv_bias)?)?;
+        let xx=conv.narrow(1,0,md)?.reshape((n,c.mamba_heads,hd))?;
+        let bb=conv.narrow(1,md,c.groups*c.state)?.reshape((n,c.groups,c.state))?;
+        let cc=conv.narrow(1,md+c.groups*c.state,c.groups*c.state)?.reshape((n,c.groups,c.state))?;
+        let dt_bias=self.w(&format!("{prefix}mamba.dt_bias"));
+        let alog=self.w(&format!("{prefix}mamba.A_log"));
+        let dskip=self.w(&format!("{prefix}mamba.D")).reshape((c.mamba_heads,1))?;
+        let neg_a=alog.exp()?.neg()?;
+        let mut state=CTensor::zeros((c.mamba_heads,hd,c.state),DType::F32,&self.device)?;
+        let mut outs=Vec::with_capacity(n);
+        for t in 0..n {
+            let u=xx.narrow(0,t,1)?.squeeze(0)?;  // [heads,head_dim]
+            let b=bb.narrow(0,t,1)?.squeeze(0)?.index_select(&self.group_index,0)?;
+            let cv=cc.narrow(0,t,1)?.squeeze(0)?.index_select(&self.group_index,0)?;
+            let dt_t=dt.narrow(0,t,1)?.squeeze(0)?.broadcast_add(dt_bias)?;
+            let step=dt_t.exp()?.affine(1.,1.)?.log()?; // softplus
+            let decay=step.broadcast_mul(&neg_a)?.exp()?.reshape((c.mamba_heads,1,1))?;
+            let step3=step.reshape((c.mamba_heads,1,1))?;
+            let bu=b.unsqueeze(1)?.broadcast_mul(&u.unsqueeze(2)?)?;
+            state=state.broadcast_mul(&decay)?.broadcast_add(&bu.broadcast_mul(&step3)?)?;
+            let y=state.broadcast_mul(&cv.unsqueeze(1)?)?.sum(2)?
+                .broadcast_add(&u.broadcast_mul(&dskip)?)?;
+            let z=gate.narrow(0,t,1)?.reshape((c.mamba_heads,hd))?;
+            let y=y.broadcast_mul(&ops::silu(&z)?)?.reshape((1,md))?;
+            outs.push(y);
+        }
+        let scan=CTensor::cat(&outs,0)?;
+        self.linear(&scan,&format!("{prefix}mamba.out_proj.weight"))?
+            .affine(c.ssm_out_multiplier as f64,0.)
+    }
+    fn forward(&self,ids:&[usize])->Result<CTensor>{
+        let c=&self.config;
+        let n=ids.len();
+        let indices=CTensor::from_vec(ids.iter().map(|&i|i as u32).collect::<Vec<_>>(),n,&self.device)?;
+        let mut x=self.w("model.embed_tokens.weight").index_select(&indices,0)?
+            .affine(c.embedding_multiplier as f64,0.)?;
+        let (cos,sin)=self.rope_tables(n)?;
+        let mut triangle=vec![0f32;n*n];
+        for t in 0..n {for j in t+1..n {triangle[t*n+j]=-1.0e9;}}
+        let mask=CTensor::from_vec(triangle,(n,n),&self.device)?;
+        for layer in 0..c.layers {
+            let p=format!("model.layers.{layer}.");
+            let h=self.norm(&x,&format!("{p}input_layernorm.weight"))?;
+            let attention=self.attention(&h,&p,n,&cos,&sin,&mask)?;
+            let ssm=self.mamba(&h,&p,n)?;
+            x=x.broadcast_add(&attention)?.broadcast_add(&ssm)?;
+            let ff=self.norm(&x,&format!("{p}pre_ff_layernorm.weight"))?;
+            let up=self.linear(&ff,&format!("{p}feed_forward.up_proj.weight"))?;
+            let gate=self.linear(&ff,&format!("{p}feed_forward.gate_proj.weight"))?
+                .affine(c.mlp_multipliers[0] as f64,0.)?;
+            let activated=ops::silu(&gate)?.broadcast_mul(&up)?;
+            let projected=self.linear(&activated,&format!("{p}feed_forward.down_proj.weight"))?
+                .affine(c.mlp_multipliers[1] as f64,0.)?;
+            x=x.broadcast_add(&projected)?;
+        }
+        let last=x.narrow(0,n-1,1)?;
+        let h=self.norm(&last,"model.final_layernorm.weight")?;
+        self.linear(&h,"lm_head.weight")?.affine(c.lm_head_multiplier as f64,0.)
+    }
+    pub fn logits(&self,ids:&[usize])->std::result::Result<Vec<f32>,String>{
+        if ids.is_empty() || ids.len()>2048 {return Err("input length must be 1..=2048".into());}
+        if ids.iter().any(|&id| id>=self.config.vocab) {return Err("token id outside vocabulary".into());}
+        self.forward(ids).and_then(|t|t.squeeze(0)?.to_vec1::<f32>()).map_err(|e|e.to_string())
+    }
+    pub fn generate(&self,ids:&[usize],count:usize)->std::result::Result<Vec<usize>,String>{
+        if count>64 || ids.len()+count>2048 {return Err("generation exceeds limits".into())}
+        let mut result=ids.to_vec();
+        for _ in 0..count {
+            let logits=self.logits(&result)?;
+            let next=logits.iter().enumerate().max_by(|a,b|a.1.total_cmp(b.1))
+                .ok_or("empty vocabulary")?.0;
+            result.push(next);
+        }
+        Ok(result)
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::{synthetic_weights,Model};
+    #[test]
+    fn small_cpu_candle_matches_reference() {
+        let c=Config::demo();let w=synthetic_weights(&c);
+        let reference=Model::new(c.clone(),w.clone()).unwrap();
+        let candle=CandleModel::new(c,w,Device::Cpu).unwrap();
+        let a=reference.logits(&[1,2,3]).unwrap();
+        let b=candle.logits(&[1,2,3]).unwrap();
+        let max_diff=a.iter().zip(&b).map(|(x,y)|(x-y).abs()).fold(0f32,f32::max);
+        assert!(max_diff < 1e-3,"max difference {max_diff}");
+    }
+    #[test]
+    fn official_05b_shape_validation() {
+        assert!(Config::falcon_h1_05b().validate().is_ok());
+    }
+}

src/lib.rs

@@ -0,0 +1,316 @@
+//! CPU reference implementation of Falcon-H1 inference mathematics, not a CUDA backend.
+//! Checkpoint tensors are loaded from the simple FH1W/f32 pack produced by tools/export.py.
+//! Intended as a readable, testable baseline; not optimized for 0.5B CPU throughput.
+use std::collections::HashMap;
+use std::fs::File;
+use std::io::{self, Read};
+
+#[derive(Clone, Debug)]
+pub struct Config {
+    pub vocab: usize, pub hidden: usize, pub layers: usize, pub heads: usize,
+    pub kv_heads: usize, pub head_dim: usize, pub mamba_heads: usize,
+    pub mamba_dim: usize, pub state: usize, pub groups: usize, pub conv: usize,
+    pub ff: usize, pub rope_theta: f32, pub eps: f32,
+    pub embedding_multiplier: f32, pub key_multiplier: f32,
+    pub attention_in_multiplier: f32, pub attention_out_multiplier: f32,
+    pub ssm_in_multiplier: f32, pub ssm_out_multiplier: f32,
+    pub ssm_multipliers: [f32;5], pub mlp_multipliers: [f32;2],
+    pub lm_head_multiplier: f32,
+}
+impl Config {
+    pub fn falcon_h1_05b() -> Self { Self {
+        vocab:32784, hidden:1024, layers:36, heads:8, kv_heads:2, head_dim:64,
+        mamba_heads:24, mamba_dim:1536, state:128, groups:1, conv:4, ff:2048,
+        rope_theta:1e11, eps:1e-5,
+        embedding_multiplier:5.656854249492381, key_multiplier:0.390625,
+        attention_in_multiplier:1., attention_out_multiplier:0.9375,
+        ssm_in_multiplier:1.25, ssm_out_multiplier:0.23570226039551587,
+        ssm_multipliers:[0.3535533905932738,0.25,0.3535533905932738,0.5,0.3535533905932738],
+        mlp_multipliers:[0.8838834764831844,0.5859375], lm_head_multiplier:0.0390625,
+    }}
+    pub fn demo() -> Self { Self {
+        vocab:64, hidden:32, layers:2, heads:4, kv_heads:2, head_dim:8,
+        mamba_heads:4, mamba_dim:32, state:8, groups:1, conv:4, ff:64,
+        rope_theta:1e11, eps:1e-5,
+        embedding_multiplier:1., key_multiplier:1., attention_in_multiplier:1.,
+        attention_out_multiplier:1., ssm_in_multiplier:1., ssm_out_multiplier:1.,
+        ssm_multipliers:[1.;5], mlp_multipliers:[1.;2], lm_head_multiplier:1.,
+    }}
+    pub fn validate(&self) -> Result<(), String> {
+        if self.heads==0 || self.kv_heads==0 || self.mamba_heads==0 || self.groups==0 || self.conv==0 || self.hidden==0 || self.vocab==0 || self.state==0 { return Err("zero dimension".into()) }
+        if self.heads % self.kv_heads != 0 || self.head_dim % 2 != 0 { return Err("invalid GQA/RoPE shape".into()) }
+        if self.mamba_dim % self.mamba_heads != 0 || self.mamba_heads % self.groups != 0 { return Err("invalid Mamba head/group shape".into()) }
+        // Falcon-H1 attention is narrower than the hidden state: 8*64 != 1024 for 0.5B.
+        if self.head_dim==0 || self.mamba_dim==0 || self.ff==0 { return Err("zero projection dimension".into()) }
+        Ok(())
+    }
+}
+
+#[derive(Clone, Debug)]
+pub struct Tensor { pub shape: Vec<usize>, pub data: Vec<f32> }
+impl Tensor {
+    fn len(&self) -> usize { self.data.len() }
+}
+pub type Weights = HashMap<String,Tensor>;
+fn bad(e: &str) -> io::Error { io::Error::new(io::ErrorKind::InvalidData,e) }
+fn take_u32(r:&mut impl Read) -> io::Result<u32> { let mut a=[0u8;4]; r.read_exact(&mut a)?; Ok(u32::from_le_bytes(a)) }
+
+/// f32 pack: magic FH1W, u32 count, then each tensor: u16 name_len, name,
+/// u8 ndim, u32 dims[ndim], little endian f32 values. No pickle/unsafe code.
+pub fn load_pack(path:&str)->io::Result<Weights>{
+    let mut f=File::open(path)?; let mut magic=[0u8;4]; f.read_exact(&mut magic)?;
+    if &magic!=b"FH1W" { return Err(bad("not a FH1W checkpoint")) }
+    let count=take_u32(&mut f)? as usize;
+    if count>50000 { return Err(bad("unreasonable tensor count")) }
+    let mut weights=HashMap::new();
+    for _ in 0..count {
+        let mut two=[0u8;2]; f.read_exact(&mut two)?;let n=u16::from_le_bytes(two) as usize;
+        if n==0 || n>1024 { return Err(bad("invalid name length")) }
+        let mut name=vec![0u8;n];f.read_exact(&mut name)?;
+        let name=String::from_utf8(name).map_err(|_|bad("invalid utf8"))?;
+        let mut one=[0u8];f.read_exact(&mut one)?;let d=one[0] as usize;
+        if d==0 || d>4 { return Err(bad("invalid rank")) }
+        let mut shape=vec![];let mut len=1usize;
+        for _ in 0..d {let size=take_u32(&mut f)? as usize;
+            len=len.checked_mul(size).ok_or_else(||bad("tensor overflow"))?;
+            shape.push(size);
+        }
+        if len>1_000_000_000 {return Err(bad("unreasonable tensor size"))}
+        let mut bytes=vec![0u8;len.checked_mul(4).ok_or_else(||bad("size overflow"))?]; f.read_exact(&mut bytes)?;
+        let data=bytes.chunks_exact(4).map(|v|f32::from_le_bytes([v[0],v[1],v[2],v[3]])).collect();
+        if weights.insert(name,Tensor{shape,data}).is_some(){return Err(bad("duplicate tensor"))}
+    }
+    Ok(weights)
+}
+
+pub fn expected_shapes(c:&Config)->HashMap<String,Vec<usize>>{
+    let mut m=HashMap::new();
+    m.insert("model.embed_tokens.weight".into(),vec![c.vocab,c.hidden]);
+    m.insert("model.final_layernorm.weight".into(),vec![c.hidden]);
+    m.insert("lm_head.weight".into(),vec![c.vocab,c.hidden]);
+    let cd=c.mamba_dim+2*c.groups*c.state;
+    for l in 0..c.layers {
+        let p=format!("model.layers.{l}.");
+        for n in ["input_layernorm.weight","pre_ff_layernorm.weight"] {m.insert(format!("{p}{n}"),vec![c.hidden]);}
+        for (n,r,k) in [
+            ("self_attn.q_proj.weight",c.heads*c.head_dim,c.hidden),
+            ("self_attn.k_proj.weight",c.kv_heads*c.head_dim,c.hidden),
+            ("self_attn.v_proj.weight",c.kv_heads*c.head_dim,c.hidden),
+            ("self_attn.o_proj.weight",c.hidden,c.heads*c.head_dim),
+            ("feed_forward.gate_proj.weight",c.ff,c.hidden),
+            ("feed_forward.up_proj.weight",c.ff,c.hidden),
+            ("feed_forward.down_proj.weight",c.hidden,c.ff),
+            ("mamba.in_proj.weight",c.mamba_dim+cd+c.mamba_heads,c.hidden),
+            ("mamba.out_proj.weight",c.hidden,c.mamba_dim),
+        ] {m.insert(format!("{p}{n}"),vec![r,k]);}
+        for n in ["mamba.dt_bias","mamba.A_log","mamba.D"]{m.insert(format!("{p}{n}"),vec![c.mamba_heads]);}
+        m.insert(format!("{p}mamba.conv1d.weight"),vec![cd,1,c.conv]);
+        m.insert(format!("{p}mamba.conv1d.bias"),vec![cd]);
+    }
+    m
+}
+pub fn validate_weights(c:&Config,w:&Weights)->Result<(),String>{
+    c.validate()?;
+    for (k,s) in expected_shapes(c) {
+        let t=w.get(&k).ok_or_else(||format!("missing tensor {k}"))?;
+        if t.shape!=s { return Err(format!("bad shape for {k}: expected {s:?}, got {:?}",t.shape)) }
+        if t.shape.iter().product::<usize>()!=t.len(){return Err(format!("bad data length for {k}"))}
+    }
+    Ok(())
+}
+fn silu(x:f32)->f32 { x/(1.+(-x).exp()) }
+fn softplus(x:f32)->f32 { if x>20. {x} else {(1.+x.exp()).ln()} }
+fn linear(x:&[f32],w:&Tensor)->Vec<f32>{
+    let (rows,cols)=(w.shape[0],w.shape[1]);debug_assert_eq!(cols,x.len());
+    let mut y=vec![0.;rows];
+    for i in 0..rows {let mut v=0f32;for j in 0..cols{v+=w.data[i*cols+j]*x[j];}y[i]=v;}
+    y
+}
+fn norm(x:&[f32],weight:&Tensor,eps:f32)->Vec<f32>{
+    let rms=(x.iter().map(|v|v*v).sum::<f32>()/(x.len() as f32)+eps).sqrt();
+    x.iter().zip(&weight.data).map(|(a,b)|a/rms*b).collect()
+}
+fn rope(x:&mut [f32],pos:usize,theta:f32){
+    let half=x.len()/2;
+    for i in 0..half {
+        let angle=(pos as f32)/theta.powf(2.*i as f32/x.len() as f32);
+        let (s,c)=angle.sin_cos();let (a,b)=(x[i],x[i+half]);
+        x[i]=a*c-b*s; x[i+half]=b*c+a*s;
+    }
+}
+fn add_assign(x:&mut[f32],y:&[f32]) {for (a,b) in x.iter_mut().zip(y){*a+=b}}
+
+/// Reference full-prefix forward. Each layer runs GQA and Mamba-2 from the SAME
+/// RMS-normalized activations, then SwiGLU after a second RMSNorm.
+/// No KV/SSM caching yet; per-request inference recomputes the prefix.
+pub struct Model {pub config:Config, pub weights:Weights}
+impl Model {
+    pub fn new(config:Config,weights:Weights)->Result<Self,String>{validate_weights(&config,&weights)?;Ok(Self{config,weights})}
+    fn t(&self,key:&str)->&Tensor {&self.weights[key]}
+    pub fn logits(&self,ids:&[usize])->Result<Vec<f32>,String>{
+        let c=&self.config;
+        if ids.is_empty(){return Err("input_ids must not be empty".into())}
+        if ids.len()>16384{return Err("sequence too long".into())}
+        for &i in ids {if i>=c.vocab{return Err(format!("token {i} outside vocabulary"))}}
+        let embeddings=self.t("model.embed_tokens.weight");
+        let mut x:Vec<Vec<f32>>=ids.iter().map(|id| embeddings.data[id*c.hidden..(id+1)*c.hidden].iter().map(|v|v*c.embedding_multiplier).collect()).collect();
+        let n=x.len();let s=c.state;let md=c.mamba_dim;let hd=md/c.mamba_heads;
+        let cd=md+2*c.groups*s;
+        for layer in 0..c.layers {
+            let p=format!("model.layers.{layer}.");
+            let normalized:Vec<Vec<f32>>=x.iter().map(|a|norm(a,self.t(&format!("{p}input_layernorm.weight")),c.eps)).collect();
+            // Mamba's causal depthwise conv and selective SSM scan.
+            let mut conv_history=vec![vec![0f32;c.conv];cd];
+            // [head, head_dim, state]
+            let mut recurrent=vec![0f32;c.mamba_heads*hd*s];
+            let mut mamba_out=vec![vec![0f32;c.hidden];n];
+            let in_proj=self.t(&format!("{p}mamba.in_proj.weight"));
+            let out_proj=self.t(&format!("{p}mamba.out_proj.weight"));
+            let cw=self.t(&format!("{p}mamba.conv1d.weight"));
+            let cb=self.t(&format!("{p}mamba.conv1d.bias"));
+            let a_log=self.t(&format!("{p}mamba.A_log"));
+            let dt_bias=self.t(&format!("{p}mamba.dt_bias"));
+            let d_skip=self.t(&format!("{p}mamba.D"));
+            let m=c.ssm_multipliers;
+            for time in 0..n {
+                let input:Vec<f32>=normalized[time].iter().map(|v|v*c.ssm_in_multiplier).collect();
+                let mut projected=linear(&input,in_proj);
+                for (i,v) in projected.iter_mut().enumerate(){
+                    let scale=if i<md{m[0]}else if i<2*md{m[1]}else if i<2*md+c.groups*s{m[2]}
+                        else if i<2*md+2*c.groups*s{m[3]}else{m[4]}; *v*=scale;
+                }
+                let gate=&projected[..md]; let conv_input=&projected[md..md+cd];
+                let dt=&projected[md+cd..];
+                let mut conv_output=vec![0f32;cd];
+                for channel in 0..cd {
+                    conv_history[channel].rotate_left(1);
+                    conv_history[channel][c.conv-1]=conv_input[channel];
+                    let mut v=cb.data[channel];
+                    // PyTorch Conv1d kernel orders oldest->newest.
+                    for k in 0..c.conv{v+=cw.data[channel*c.conv+k]*conv_history[channel][k]}
+                    conv_output[channel]=silu(v);
+                }
+                let xx=&conv_output[..md];
+                let b=&conv_output[md..md+c.groups*s];
+                let cc=&conv_output[md+c.groups*s..];
+                let mut output=vec![0f32;md];
+                for h in 0..c.mamba_heads {
+                    let group=h/(c.mamba_heads/c.groups);
+                    let step=softplus(dt[h]+dt_bias.data[h]);
+                    let decay=(-a_log.data[h].exp()*step).exp();
+                    for k in 0..hd {
+                        let feature=h*hd+k;
+                        let u=xx[feature];
+                        let mut y=d_skip.data[h]*u;
+                        for st in 0..s {
+                            let i=feature*s+st;
+                            recurrent[i]=decay*recurrent[i]+step*b[group*s+st]*u;
+                            y+=cc[group*s+st]*recurrent[i];
+                        }
+                        output[feature]=y*silu(gate[feature]);
+                    }
+                }
+                mamba_out[time]=linear(&output,out_proj).iter().map(|v|v*c.ssm_out_multiplier).collect();
+            }
+            // GQA: full-prefix causal softmax, per-query RoPE and K multiplier.
+            let mut q=vec![vec![0f32;c.heads*c.head_dim];n];
+            let mut k=vec![vec![0f32;c.kv_heads*c.head_dim];n];
+            let mut v=vec![vec![0f32;c.kv_heads*c.head_dim];n];
+            for t in 0..n {
+                let a:Vec<f32>=normalized[t].iter().map(|x|x*c.attention_in_multiplier).collect();
+                q[t]=linear(&a,self.t(&format!("{p}self_attn.q_proj.weight")));
+                k[t]=linear(&a,self.t(&format!("{p}self_attn.k_proj.weight")));
+                v[t]=linear(&a,self.t(&format!("{p}self_attn.v_proj.weight")));
+                for head in 0..c.heads {rope(&mut q[t][head*c.head_dim..(head+1)*c.head_dim],t,c.rope_theta)}
+                for head in 0..c.kv_heads {
+                    let chunk=&mut k[t][head*c.head_dim..(head+1)*c.head_dim];
+                    for x in chunk.iter_mut(){*x*=c.key_multiplier;}
+                    rope(chunk,t,c.rope_theta);
+                }
+            }
+            for t in 0..n {
+                let mut y=vec![0f32;c.heads*c.head_dim];
+                for h in 0..c.heads {
+                    let kv=h/(c.heads/c.kv_heads);
+                    let qi=&q[t][h*c.head_dim..(h+1)*c.head_dim];
+                    let mut scores=Vec::with_capacity(t+1);
+                    for j in 0..=t {
+                        let kj=&k[j][kv*c.head_dim..(kv+1)*c.head_dim];
+                        scores.push(qi.iter().zip(kj).map(|(a,b)|a*b).sum::<f32>()/(c.head_dim as f32).sqrt());
+                    }
+                    let mx=scores.iter().copied().fold(f32::NEG_INFINITY,f32::max);
+                    let exps:Vec<f32>=scores.iter().map(|z|(z-mx).exp()).collect();
+                    let denom=exps.iter().sum::<f32>();
+                    for j in 0..=t {
+                        let vv=&v[j][kv*c.head_dim..(kv+1)*c.head_dim];
+                        for d in 0..c.head_dim{y[h*c.head_dim+d]+=exps[j]/denom*vv[d];}
+                    }
+                }
+                let attn=linear(&y,self.t(&format!("{p}self_attn.o_proj.weight")));
+                for i in 0..c.hidden {x[t][i]+=mamba_out[t][i]+c.attention_out_multiplier*attn[i];}
+            }
+            // pre-FF RMSNorm + SwiGLU with Falcon-H1 µP multipliers
+            for t in 0..n {
+                let a=norm(&x[t],self.t(&format!("{p}pre_ff_layernorm.weight")),c.eps);
+                let up=linear(&a,self.t(&format!("{p}feed_forward.up_proj.weight")));
+                let gate=linear(&a,self.t(&format!("{p}feed_forward.gate_proj.weight")));
+                let z:Vec<f32>=up.iter().zip(&gate).map(|(u,g)|u*silu(g*c.mlp_multipliers[0])).collect();
+                let ff=linear(&z,self.t(&format!("{p}feed_forward.down_proj.weight")));
+                for i in 0..c.hidden{x[t][i]+=ff[i]*c.mlp_multipliers[1]}
+            }
+        }
+        let final_h=norm(x.last().unwrap(),self.t("model.final_layernorm.weight"),c.eps);
+        Ok(linear(&final_h,self.t("lm_head.weight")).iter().map(|z|z*c.lm_head_multiplier).collect())
+    }
+    pub fn generate(&self,ids:&[usize],count:usize)->Result<Vec<usize>,String>{
+        if count>64 {return Err("max_new_tokens limited to 64 in CPU reference".into())}
+        let mut sequence=ids.to_vec();
+        for _ in 0..count {
+            let y=self.logits(&sequence)?;
+            let next=y.iter().enumerate().max_by(|a,b|a.1.total_cmp(b.1)).unwrap().0;
+            sequence.push(next);
+        }
+        Ok(sequence)
+    }
+}
+
+/// Deterministic tiny tensors for API unit tests. Not a pretrained model.
+pub fn synthetic_weights(c:&Config)->Weights{
+    let mut w=HashMap::new();let mut seed:u64=0x9e3779b97f4a7c15;
+    for (name,shape) in expected_shapes(c){
+        let mut data=vec![0f32;shape.iter().product()];
+        for v in &mut data {seed ^= seed>>12;seed ^= seed<<25;seed ^= seed>>27;
+            let z=(seed.wrapping_mul(2685821657736338717)>>40) as f32/(1u32<<24) as f32;
+            *v=(z-0.5)*0.08;}
+        if name.ends_with("layernorm.weight") || name.ends_with("final_layernorm.weight") {
+            data.fill(1.);
+        }
+        if name.ends_with("mamba.D") {data.fill(1.);}
+        if name.ends_with("mamba.dt_bias") {data.fill(0.);}
+        if name.ends_with("mamba.A_log") {for (i,x) in data.iter_mut().enumerate(){*x=((i+1) as f32).ln()}}
+        w.insert(name,Tensor{shape,data});
+    }
+    w
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    #[test] fn test_falcon_dimensions(){assert!(Config::falcon_h1_05b().validate().is_ok());}
+    #[test] fn test_cpu_logits(){let c=Config::demo();let m=Model::new(c.clone(),synthetic_weights(&c)).unwrap();let y=m.logits(&[1,2,3]).unwrap();assert_eq!(y.len(),c.vocab);assert!(y.iter().all(|x|x.is_finite()));}
+    #[test] fn test_causal_prefix(){let c=Config::demo();let m=Model::new(c.clone(),synthetic_weights(&c)).unwrap();
+        let a=m.logits(&[1,2]).unwrap();let b=m.logits(&[0,1,2]).unwrap();
+        assert_eq!(a.len(),b.len()); // shifted positions legitimately alter RoPE
+        assert!(m.logits(&[1,2,3]).is_ok());}
+    #[test] fn test_determinism(){let c=Config::demo();let m=Model::new(c.clone(),synthetic_weights(&c)).unwrap();
+        assert_eq!(m.logits(&[3,5,7]).unwrap(),m.logits(&[3,5,7]).unwrap());}
+    #[test] fn test_generate(){let c=Config::demo();let m=Model::new(c.clone(),synthetic_weights(&c)).unwrap();
+        assert_eq!(m.generate(&[1,2],2).unwrap().len(),4);}
+    #[test] fn test_bad_token(){let c=Config::demo();let m=Model::new(c.clone(),synthetic_weights(&c)).unwrap();
+        assert!(m.logits(&[c.vocab]).is_err());}
+    #[test] fn test_weight_validation(){let c=Config::demo();let mut w=synthetic_weights(&c);
+        w.remove("model.embed_tokens.weight");assert!(Model::new(c,w).is_err());}
+}
+
+#[cfg(feature = "candle")]
+pub mod candle_backend;

src/main.rs

@@ -0,0 +1,149 @@
+//! Minimal dependency-free HTTP/1.1 API for token-ID-based Falcon-H1 inference.
+//! For localhost/Colab tunnel demo only; deploy behind a reverse proxy for production.
+use falcon_h1_rust::{load_pack,synthetic_weights,Config,Model};
+use std::io::{Read,Write};
+use std::net::{TcpListener,TcpStream};
+use std::sync::Arc;
+use std::time::Duration;
+
+fn extract_array(body:&str,key:&str)->Result<Vec<usize>,String>{
+    let needle=format!("\"{key}\"");
+    let idx=body.find(&needle).ok_or_else(||format!("missing {key}"))?;
+    let tail=&body[idx+needle.len()..];
+    let colon=tail.find(':').ok_or("missing :")?;
+    let tail=&tail[colon+1..];
+    let start=tail.find('[').ok_or("missing [")?;
+    let tail=&tail[start+1..];
+    let end=tail.find(']').ok_or("missing ]")?;
+    let data=tail[..end].trim();
+    if data.is_empty(){return Err("empty input_ids".into())}
+    let ids=data.split(',').map(|x|x.trim().parse::<usize>().map_err(|_|"invalid token ID".to_string())).collect::<Result<Vec<_>,_>>()?;
+    if ids.len()>2048{return Err("sequence exceeds API limit (2048 tokens)".into())}
+    Ok(ids)
+}
+fn extract_number(body:&str,key:&str,default:usize)->Result<usize,String>{
+    let needle=format!("\"{key}\"");
+    if let Some(idx)=body.find(&needle){
+        let tail=&body[idx+needle.len()..];
+        let colon=tail.find(':').ok_or("missing :")?;
+        let num=tail[colon+1..].trim_start().chars().take_while(|c|c.is_ascii_digit()).collect::<String>();
+        if num.is_empty(){return Err(format!("invalid {key}"))}
+        num.parse::<usize>().map_err(|_|format!("invalid {key}"))
+    } else {Ok(default)}
+}
+fn response(mut stream:TcpStream,code:&str,payload:&str){
+    let msg=format!("HTTP/1.1 {code}\r\nContent-Type: application/json\r\nContent-Length: {}\r\nConnection: close\r\nAccess-Control-Allow-Origin: *\r\nAccess-Control-Allow-Methods: GET, POST, OPTIONS\r\nAccess-Control-Allow-Headers: Content-Type\r\n\r\n{}",payload.len(),payload);
+    let _=stream.write_all(msg.as_bytes());
+}
+fn json_err(e:&str)->String{format!("{{\"error\":\"{}\"}}",e.replace('\\',"\\\\").replace('"',"\\\"").replace('\n'," "))}
+enum Backend {
+    Cpu(Model),
+    #[cfg(feature="candle")]
+    Candle(falcon_h1_rust::candle_backend::CandleModel),
+}
+impl Backend {
+    fn config(&self)->&Config { match self {
+        Self::Cpu(m)=>&m.config,
+        #[cfg(feature="candle")]
+        Self::Candle(m)=>&m.config,
+    }}
+    fn label(&self)->&'static str {match self {
+        Self::Cpu(_)=>"cpu-reference",
+        #[cfg(feature="candle")]
+        Self::Candle(m)=>m.device_name(),
+    }}
+    fn logits(&self,ids:&[usize])->Result<Vec<f32>,String>{match self {
+        Self::Cpu(m)=>m.logits(ids),
+        #[cfg(feature="candle")]
+        Self::Candle(m)=>m.logits(ids),
+    }}
+    fn generate(&self,ids:&[usize],n:usize)->Result<Vec<usize>,String>{match self {
+        Self::Cpu(m)=>m.generate(ids,n),
+        #[cfg(feature="candle")]
+        Self::Candle(m)=>m.generate(ids,n),
+    }}
+}
+fn handle(mut stream:TcpStream,model:Arc<Backend>){
+    let _=stream.set_read_timeout(Some(Duration::from_secs(15)));
+    let _=stream.set_write_timeout(Some(Duration::from_secs(15)));
+    let mut buf=vec![0u8;16384];let mut len=0usize;let mut expected=None;
+    loop {
+        if len==buf.len(){response(stream,"413 Payload Too Large",&json_err("request too large"));return}
+        match stream.read(&mut buf[len..]) {
+            Ok(0)=>break,
+            Ok(n)=>len+=n,
+            Err(_)=>return,
+        }
+        if let Some(hend)=buf[..len].windows(4).position(|w|w==b"\r\n\r\n") {
+            if expected.is_none(){
+                let headers=String::from_utf8_lossy(&buf[..hend]);
+                let size=headers.lines().filter_map(|l|l.split_once(':')).find(|(k,_)|k.eq_ignore_ascii_case("content-length"))
+                    .and_then(|(_,v)|v.trim().parse::<usize>().ok()).unwrap_or(0);
+                expected=Some(hend+4+size);
+            }
+            if len>=expected.unwrap(){break}
+        }
+    }
+    let raw=String::from_utf8_lossy(&buf[..len]);
+    let first=raw.lines().next().unwrap_or("");
+    let (method,path) = {let mut p=first.split_whitespace();(p.next().unwrap_or(""),p.next().unwrap_or(""))};
+    if method=="OPTIONS" {response(stream,"204 No Content","");return}
+    if method=="GET" && path=="/health" {response(stream,"200 OK",&format!("{{\"status\":\"ok\",\"backend\":\"{}\"}}",model.label()));return}
+    if method=="GET" && path=="/v1/model" {
+        let c=model.config();
+        let msg=format!("{{\"architecture\":\"falcon_h1\",\"backend\":\"{}\",\"layers\":{},\"hidden\":{},\"attention_heads\":{},\"kv_heads\":{},\"mamba_heads\":{},\"vocab_size\":{}}}",model.label(),c.layers,c.hidden,c.heads,c.kv_heads,c.mamba_heads,c.vocab);
+        response(stream,"200 OK",&msg);return
+    }
+    if method!="POST" || (path!="/v1/logits" && path!="/v1/generate") {
+        response(stream,"404 Not Found",&json_err("unknown route"));return
+    }
+    let body=match raw.split_once("\r\n\r\n") {Some((_,v))=>v,None=>{response(stream,"400 Bad Request",&json_err("no body"));return}};
+    let result = (||->Result<String,String>{
+        let ids=extract_array(body,"input_ids")?;
+        if path=="/v1/logits" {
+            let y=model.logits(&ids)?;
+            let text=y.iter().map(|x|format!("{x:.7}")).collect::<Vec<_>>().join(",");
+            Ok(format!("{{\"logits\":[{text}],\"vocab_size\":{}}}",y.len()))
+        } else {
+            let count=extract_number(body,"max_new_tokens",1)?;
+            let seq=model.generate(&ids,count)?;
+            Ok(format!("{{\"output_ids\":[{}],\"generated_tokens\":{count}}}",seq.iter().map(|x|x.to_string()).collect::<Vec<_>>().join(",")))
+        }
+    })();
+    match result{Ok(msg)=>response(stream,"200 OK",&msg),Err(e)=>response(stream,"400 Bad Request",&json_err(&e))}
+}
+fn main()->Result<(),Box<dyn std::error::Error>>{
+    let args:Vec<String>=std::env::args().collect();
+    let demo=args.iter().any(|x|x=="--demo");
+    let bind=args.iter().position(|x|x=="--bind").and_then(|i|args.get(i+1)).map(|s|s.as_str()).unwrap_or("127.0.0.1:8080");
+    let c=if demo{Config::demo()}else{Config::falcon_h1_05b()};
+    let weights=if demo{synthetic_weights(&c)}else{
+        let path=args.iter().position(|x|x=="--checkpoint").and_then(|i|args.get(i+1)).ok_or("use --checkpoint path/to/fh1.f32pack or --demo")?;
+        load_pack(path)?
+    };
+    let device=args.iter().position(|x|x=="--device").and_then(|i|args.get(i+1)).map(|s|s.as_str()).unwrap_or("cpu");
+    let model=match device {
+        "cpu"=>Backend::Cpu(Model::new(c,weights)?),
+        #[cfg(feature="candle")]
+        "cpu-candle"=>Backend::Candle(falcon_h1_rust::candle_backend::CandleModel::new(c,weights,candle_core::Device::Cpu)?),
+        #[cfg(feature="cuda")]
+        "cuda"=>Backend::Candle(falcon_h1_rust::candle_backend::CandleModel::new(c,weights,candle_core::Device::new_cuda(0)?)?),
+        _=>return Err(format!("unsupported --device {device}; build with --features cuda for CUDA").into()),
+    };
+    let model=Arc::new(model);
+    let listener=TcpListener::bind(bind)?;
+    eprintln!("Falcon-H1 Rust API listening on http://{bind} (demo={demo}, backend={})",model.label());
+    for request in listener.incoming(){
+        if let Ok(stream)=request {let shared=Arc::clone(&model);
+            std::thread::spawn(move||handle(stream,shared));}
+    }
+    Ok(())
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    #[test] fn parse_ids(){ assert_eq!(extract_array(r#"{"input_ids":[1,2,3]}"#,"input_ids").unwrap(),vec![1,2,3]); }
+    #[test] fn reject_ids(){assert!(extract_array(r#"{"input_ids":[1,"x"]}"#,"input_ids").is_err());}
+    #[test] fn parse_count(){ assert_eq!(extract_number(r#"{"max_new_tokens":3}"#,"max_new_tokens",1).unwrap(),3); }
+}

tools/client.py

@@ -0,0 +1,30 @@
+"""Call the Rust API with an ordinary text prompt (tokenizer runs in the client).
+
+Usage:
+  pip install transformers requests
+  python tools/client.py --prompt 'Hello' --max-new-tokens 12
+
+Synthetic --demo weights cannot produce meaningful text. Run with a real checkpoint.
+"""
+import argparse
+import requests
+from transformers import AutoTokenizer
+
+
+def main():
+    p = argparse.ArgumentParser()
+    p.add_argument('--api', default='http://127.0.0.1:8080')
+    p.add_argument('--repo', default='tiiuae/Falcon-H1-0.5B-Instruct')
+    p.add_argument('--prompt', default='Hello!')
+    p.add_argument('--max-new-tokens', type=int, default=8)
+    args = p.parse_args()
+    tok = AutoTokenizer.from_pretrained(args.repo)
+    ids = tok.encode(args.prompt, add_special_tokens=True)
+    r = requests.post(args.api+'/v1/generate', json={'input_ids': ids,
+                       'max_new_tokens': args.max_new_tokens}, timeout=1200)
+    r.raise_for_status()
+    answer = r.json()['output_ids']
+    print(tok.decode(answer, skip_special_tokens=True))
+
+if __name__ == '__main__':
+    main()

tools/compare_hf.py

@@ -0,0 +1,36 @@
+"""Compare Rust token-ID /v1/logits with the official 0.5B Hugging Face model.
+
+Run a Rust --checkpoint server first. This parity check is deliberately a report:
+there is no unsupported numerical-parity claim until the numbers are inspected.
+"""
+import argparse
+import json
+import urllib.request
+import numpy as np
+import torch
+from transformers import AutoModelForCausalLM
+
+
+def main():
+    p = argparse.ArgumentParser()
+    p.add_argument('--repo', default='tiiuae/Falcon-H1-0.5B-Instruct')
+    p.add_argument('--api', default='http://127.0.0.1:8080/v1/logits')
+    p.add_argument('--ids', nargs='+', type=int, default=[1, 452, 1223])
+    p.add_argument('--device', default='cuda' if torch.cuda.is_available() else 'cpu')
+    args = p.parse_args()
+    model = AutoModelForCausalLM.from_pretrained(args.repo, torch_dtype=torch.float32).eval().to(args.device)
+    with torch.no_grad():
+        y = model(input_ids=torch.tensor([args.ids], dtype=torch.long, device=args.device)).logits[0, -1, :].float().cpu().numpy()
+    req = urllib.request.Request(args.api, data=json.dumps({'input_ids': args.ids}).encode(), headers={'Content-Type':'application/json'})
+    with urllib.request.urlopen(req, timeout=600) as resp:
+        z = np.asarray(json.load(resp)['logits'], dtype='float32')
+    if y.shape != z.shape:
+        raise ValueError(f'vocab shape mismatch: HF {y.shape}, Rust {z.shape}')
+    print('reference_top1', int(np.argmax(y)))
+    print('rust_top1', int(np.argmax(z)))
+    print('max_abs_error', float(np.max(np.abs(y-z))))
+    print('mean_abs_error', float(np.mean(np.abs(y-z))))
+    print('cosine_similarity', float(np.dot(y,z) / (np.linalg.norm(y)*np.linalg.norm(z))))
+
+if __name__ == '__main__':
+    main()

tools/export.py

@@ -0,0 +1,56 @@
+"""Export local HF Falcon-H1 safetensors into exact-name f32 FH1W tensor pack.
+
+Dependencies: pip install safetensors torch huggingface_hub
+Example:
+  python tools/export.py --repo tiiuae/Falcon-H1-0.5B-Instruct --output falcon-h1.f32pack
+
+The 0.5B model is ~2 GB in float32; this is CPU reference, not a GPU converter.
+"""
+import argparse
+import struct
+from pathlib import Path
+from huggingface_hub import snapshot_download
+from safetensors import safe_open
+
+
+def convert(model_dir: Path, output: Path):
+    paths = sorted(model_dir.glob("*.safetensors"))
+    if not paths:
+        raise ValueError("No safetensors files in selected model directory")
+    # Include only model tensors; do not include unrelated training checkpoint tensors.
+    entries = []
+    for path in paths:
+        with safe_open(str(path), framework="pt", device="cpu") as sf:
+            for name in sf.keys():
+                if name.startswith(("model.", "lm_head.")):
+                    entries.append((path, name))
+    if len(entries) > 50000:
+        raise ValueError("Too many checkpoint tensors")
+    output.parent.mkdir(parents=True, exist_ok=True)
+    with output.open("wb") as f:
+        f.write(b"FH1W")
+        f.write(struct.pack("<I", len(entries)))
+        for path, name in entries:
+            with safe_open(str(path), framework="pt", device="cpu") as sf:
+                tensor = sf.get_tensor(name).float().contiguous()
+                name_b = name.encode("utf-8")
+                shape = tuple(tensor.shape)
+                if not (1 <= len(shape) <= 4):
+                    raise ValueError((name, shape))
+                f.write(struct.pack("<H", len(name_b)))
+                f.write(name_b)
+                f.write(struct.pack("<B", len(shape)))
+                for dim in shape:
+                    f.write(struct.pack("<I", dim))
+                f.write(tensor.numpy().astype("<f4", copy=False).tobytes())
+    print(f"Wrote {len(entries)} named float32 tensors: {output}")
+
+
+if __name__ == "__main__":
+    p = argparse.ArgumentParser()
+    p.add_argument("--repo", default="tiiuae/Falcon-H1-0.5B-Instruct")
+    p.add_argument("--local", help="Use an already downloaded model directory")
+    p.add_argument("--output", default="falcon-h1.f32pack")
+    a = p.parse_args()
+    root = Path(a.local) if a.local else Path(snapshot_download(repo_id=a.repo, allow_patterns=["*.safetensors", "*.json"]))
+    convert(root, Path(a.output))

tools/smoke_api.py

@@ -0,0 +1,27 @@
+"""Check actual HTTP endpoint execution, including reported device."""
+import argparse
+import json
+import urllib.request
+
+p = argparse.ArgumentParser()
+p.add_argument('--backend', default='cpu-reference')
+p.add_argument('--url', default='http://127.0.0.1:8080')
+a = p.parse_args()
+
+def request(path, data=None):
+    body = json.dumps(data).encode() if data is not None else None
+    req = urllib.request.Request(a.url+path, data=body,
+            headers={'Content-Type': 'application/json'} if body else {})
+    with urllib.request.urlopen(req, timeout=600) as resp:
+        assert resp.status == 200
+        return json.load(resp)
+
+health = request('/health')
+assert health['status'] == 'ok', health
+assert health['backend'] == a.backend, health
+info = request('/v1/model')
+assert info['architecture'] == 'falcon_h1'
+assert info['backend'] == a.backend
+assert len(request('/v1/logits', {'input_ids': [1,2,3]})['logits']) == 64
+assert len(request('/v1/generate', {'input_ids': [1,2], 'max_new_tokens':2})['output_ids']) == 4
+print('PASS:', a.backend, 'health, model, logits, generate via HTTP')