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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

.meta.json

@@ -0,0 +1,17 @@
+{
+  "source_model": {
+    "name": "RecursiveSI/flint",
+    "commit_id": "2a247be0c0aec895ccb0ac7ca92229ec6df37288"
+  },
+  "env": {
+    "python": "3.12.13",
+    "packages": {
+      "huggingface_hub": "1.27.0",
+      "safetensors": "0.8.0",
+      "tokenizers": "0.23.0rc0",
+      "torch": "2.13.0+cu126",
+      "transformers": "5.15.0.dev0"
+    },
+    "cuda": "12.6"
+  }
+}

LICENSE

@@ -0,0 +1,201 @@
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+   limitations under the License.

README.md

@@ -0,0 +1,147 @@
+---
+library_name: sglang
+base_model:
+- RecursiveSI/flint
+---
+
+This tiny model is intended for debugging. It is randomly initialized using the configuration adapted from [RecursiveSI/flint](https://huggingface.co/RecursiveSI/flint).
+
+Structural comparison with `RecursiveSI/flint`:
+
+| Feature | Source | Tiny |
+|---|---:|---:|
+| Loading implementation | source SGLang plugin | same source plugin wheel |
+| Stored precision | BF16 | BF16 |
+| Vocabulary / embedding and LM-head ties | 150,144 / untied | same |
+| Layers / dense prefix | 49 / layers 0-1 | same |
+| Attention cadence / RoPE-free layers | 49-layer source schedule | same |
+| Hidden size / Q and KV heads / head dimension | 2,048 / 32 and 4 / 128 | 32 / 1 and 1 / 32 |
+| Routed experts / active experts | 256 / 6 | 64 / 6 |
+| MoE / shared-expert widths | 512 / 1,024 | 32 / 64 |
+| Sigmoid router, top-k renormalization, learned sinks, QK norms | present | same checkpoint schema |
+
+Direct checkpoint generation is required because Transformers does not implement
+Flint.  Tensor names and shapes come from the pinned source safetensors headers
+and the source-provided `flint_sglang` loader wheel.  The source plugin explicitly
+validates TP=1 only, so this model carries no TP=4 claim.
+
+| File path | Size |
+|------|------|
+| model.safetensors | 40.1MB |
+
+
+### Example usage:
+
+- SGLang (unverified)
+
+- The source Flint plugin validates one GPU per replica only; it explicitly marks tensor/expert/pipeline parallelism as unvalidated.  The tiny checkpoint therefore does not claim TP=4 support.
+
+- Install the source-provided plugin wheel before this command.  Its learned attention sinks, sigmoid-router top-k, shared expert, tool grammar and reasoning parser are not implemented by stock SGLang.
+
+```bash
+pip install ./sglang/flint_sglang-0.1.4-py3-none-any.whl
+SGLANG_EXTERNAL_MODEL_PACKAGE=flint_sglang FLASHINFER_USE_CUDA_NORM=1 \
+python -m sglang.launch_server \
+  --model-path tiny-random/flint --served-model-name flint --tp-size 1 \
+  --attention-backend fa3 --moe-runner-backend triton --enable-fp32-lm-head \
+  --tool-call-parser flint --reasoning-parser flint --grammar-backend flint
+```
+
+### Codes to create this repo:
+
+<details><summary>Click to expand</summary>
+
+```python
+import json
+from pathlib import Path
+from shutil import copyfile
+
+import torch
+from huggingface_hub import hf_hub_download
+from safetensors.torch import save_file
+
+source_model_id = "RecursiveSI/flint"
+save_folder = "/tmp/tiny-random/flint"
+source_revision = "2a247be0c0aec895ccb0ac7ca92229ec6df37288"
+folder = Path(save_folder)
+folder.mkdir(parents=True, exist_ok=True)
+raw = json.loads(Path(hf_hub_download(source_model_id, "config.json", revision=source_revision)).read_text())
+# Transformers has no Flint model class.  The source SGLang plugin is the
+# authoritative executable schema, so create its public checkpoint tensors directly.
+# The fixed 150,144-row untied embedding and LM head make hidden_size=32 the
+# smallest practical BF16 geometry; one 32-wide attention head retains the
+# required LLM head-dimension floor.
+raw.update(
+    hidden_size=32, head_dim=32, num_attention_heads=1, num_key_value_heads=1,
+    intermediate_size=128, moe_intermediate_size=32, shared_expert_intermediate_size=64,
+    num_experts=64, num_experts_per_tok=6, max_position_embeddings=4096,
+)
+(folder / "config.json").write_text(json.dumps(raw, indent=2) + "\n", encoding="utf-8")
+generator = torch.Generator(device="cpu").manual_seed(42)
+
+def random_tensor(*shape: int) -> torch.Tensor:
+    return torch.randn(shape, generator=generator, dtype=torch.bfloat16).mul_(0.02)
+
+hidden = raw["hidden_size"]
+head_dim = raw["head_dim"]
+experts = raw["num_experts"]
+state: dict[str, torch.Tensor] = {
+    "model.embed_tokens.weight": random_tensor(raw["vocab_size"], hidden),
+    "lm_head.weight": random_tensor(raw["vocab_size"], hidden),
+    "model.norm.weight": torch.ones(hidden, dtype=torch.bfloat16),
+}
+dense_layers = set(raw["mlp_only_layers"])
+for layer in range(raw["num_hidden_layers"]):
+    prefix = f"model.layers.{layer}"
+    state[f"{prefix}.input_layernorm.weight"] = torch.ones(hidden, dtype=torch.bfloat16)
+    state[f"{prefix}.post_attention_layernorm.weight"] = torch.ones(hidden, dtype=torch.bfloat16)
+    state[f"{prefix}.self_attn.q_proj.weight"] = random_tensor(hidden, hidden)
+    state[f"{prefix}.self_attn.k_proj.weight"] = random_tensor(head_dim, hidden)
+    state[f"{prefix}.self_attn.v_proj.weight"] = random_tensor(head_dim, hidden)
+    state[f"{prefix}.self_attn.o_proj.weight"] = random_tensor(hidden, hidden)
+    state[f"{prefix}.self_attn.q_norm.weight"] = torch.ones(head_dim, dtype=torch.bfloat16)
+    state[f"{prefix}.self_attn.k_norm.weight"] = torch.ones(head_dim, dtype=torch.bfloat16)
+    state[f"{prefix}.self_attn.softmax_offset"] = random_tensor(raw["num_attention_heads"])
+    if layer in dense_layers:
+        for projection in ("gate_proj", "up_proj"):
+            state[f"{prefix}.mlp.{projection}.weight"] = random_tensor(raw["intermediate_size"], hidden)
+        state[f"{prefix}.mlp.down_proj.weight"] = random_tensor(hidden, raw["intermediate_size"])
+        continue
+    state[f"{prefix}.mlp.gate.weight"] = random_tensor(experts, hidden)
+    for projection in ("gate_proj", "up_proj"):
+        state[f"{prefix}.mlp.shared_expert.{projection}.weight"] = random_tensor(
+            raw["shared_expert_intermediate_size"], hidden
+        )
+    state[f"{prefix}.mlp.shared_expert.down_proj.weight"] = random_tensor(
+        hidden, raw["shared_expert_intermediate_size"]
+    )
+    for expert in range(experts):
+        expert_prefix = f"{prefix}.mlp.experts.{expert}"
+        for projection in ("gate_proj", "up_proj"):
+            state[f"{expert_prefix}.{projection}.weight"] = random_tensor(raw["moe_intermediate_size"], hidden)
+        state[f"{expert_prefix}.down_proj.weight"] = random_tensor(hidden, raw["moe_intermediate_size"])
+save_file(state, folder / "model.safetensors", metadata={"format": "pt"})
+for filename in (
+    "LICENSE", "chat_template.jinja", "generation_config.json", "tokenizer.json", "tokenizer_config.json",
+    "sglang/flint_sglang-0.1.4-py3-none-any.whl", "sglang/serve_h200.sh", "sglang/README.md",
+):
+    destination = folder / filename
+    destination.parent.mkdir(parents=True, exist_ok=True)
+    copyfile(hf_hub_download(source_model_id, filename, revision=source_revision), destination)
+```
+
+</details>
+
+### Test environment:
+
+- huggingface_hub: 1.27.0
+- safetensors: 0.8.0
+- tokenizers: 0.23.0rc0
+- torch: 2.13.0+cu126
+- transformers: 5.15.0.dev0
+
+### Change log:
+
+- 2026-10-23: Initial version.
+
+This repo is generated by GPT-6.1-Sol

chat_template.jinja

@@ -0,0 +1,61 @@
+{%- macro flint_text(t) -%}
+{%- if t is string -%}
+{{- t | replace("\x00", "") | replace("<think>", "") | replace("</think>", "") | replace("<|begin_of_text|>", "&lt;|begin_of_text|&gt;") | replace("<|end_of_text|>", "&lt;|end_of_text|&gt;") | replace("<|system|>", "&lt;|system|&gt;") | replace("<|developer|>", "&lt;|developer|&gt;") | replace("<|user|>", "&lt;|user|&gt;") | replace("<|assistant|>", "&lt;|assistant|&gt;") | replace("<|observation|>", "&lt;|observation|&gt;") -}}
+{%- else -%}
+{{- t -}}
+{%- endif -%}
+{%- endmacro -%}
+{%- set tool_preamble -%}
+{%- if tools %}<|system|>
+# Tools
+
+You may call one or more functions to assist with the user query.
+
+You are provided with function signatures within <tools></tools> XML tags:
+<tools>
+{%- for tool in tools %}
+{%- set fn = tool.function if tool.function is defined else tool %}
+{{ fn | tojson(ensure_ascii=False) }}
+{%- endfor %}
+</tools>
+
+For each function call, output the function name and arguments within the following XML format:
+<tool_call>{function-name}<arg_key>{arg-key-1}</arg_key><arg_value>{arg-value-1}</arg_value>...</tool_call>
+{% endif -%}
+{%- endset -%}
+{%- set ns = namespace(tools_pending=true) -%}
+{%- for m in messages -%}
+{%- if ns.tools_pending and m.role != 'system' -%}
+{{ tool_preamble }}
+{%- set ns.tools_pending = false -%}
+{%- endif -%}
+{%- if m.role == 'system' -%}
+<|system|>{{ flint_text(m.content) }}
+{%- elif m.role == 'developer' -%}
+<|developer|>{{ flint_text(m.content) }}
+{%- elif m.role == 'user' -%}
+<|user|>{{ flint_text(m.content) }}
+{%- elif m.role == 'assistant' -%}
+<|assistant|>
+{%- if m.reasoning_content %}<think>{{ m.reasoning_content }}</think>{% endif -%}
+{%- if m.content %}{{ m.content }}{% endif -%}
+{%- if m.tool_calls -%}
+{%- for tc in m.tool_calls -%}
+{%- set fn = tc.function if tc.function is defined else tc -%}
+<tool_call>{{ fn.name }}
+{%- for k, v in fn.arguments.items() -%}
+<arg_key>{{ k }}</arg_key><arg_value>{{ v | tojson(ensure_ascii=False) if v is not string else v }}</arg_value>
+{%- endfor -%}
+</tool_call>
+{%- endfor -%}
+{%- endif -%}
+{%- elif m.role == 'tool' -%}
+<|observation|><tool_response>{{ flint_text(m.content) }}</tool_response>
+{%- endif -%}
+{%- endfor -%}
+{%- if ns.tools_pending -%}
+{{ tool_preamble }}
+{%- endif -%}
+{%- if add_generation_prompt -%}
+<|assistant|>
+{%- endif -%}

config.json

@@ -0,0 +1,115 @@
+{
+  "architectures": [
+    "FlintForCausalLM"
+  ],
+  "model_type": "flint",
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 150000,
+  "decoder_sparse_step": 1,
+  "eos_token_id": [
+    150001,
+    150003,
+    150005,
+    150018
+  ],
+  "head_dim": 32,
+  "hidden_act": "silu",
+  "hidden_size": 32,
+  "initializer_range": 0.02,
+  "intermediate_size": 128,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 4096,
+  "mlp_only_layers": [
+    0,
+    1
+  ],
+  "moe_intermediate_size": 32,
+  "norm_topk_prob": true,
+  "num_attention_heads": 1,
+  "num_experts": 64,
+  "num_experts_per_tok": 6,
+  "num_hidden_layers": 49,
+  "num_key_value_heads": 1,
+  "no_rope_layer_indices": [
+    0,
+    1,
+    5,
+    9,
+    13,
+    17,
+    21,
+    25,
+    29,
+    33,
+    37,
+    41,
+    45,
+    48
+  ],
+  "output_router_logits": false,
+  "pad_token_id": 150001,
+  "rms_norm_eps": 1e-06,
+  "rope_theta": 10000.0,
+  "router_aux_loss_coef": 0.0005,
+  "router_score_function": "sigmoid",
+  "shared_expert_intermediate_size": 64,
+  "sliding_window": 513,
+  "softmax_type": "learnable",
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "use_cache": true,
+  "use_sliding_window": true,
+  "vocab_size": 150144,
+  "transformers_version": "5.12.1"
+}

generation_config.json

@@ -0,0 +1,14 @@
+{
+  "bos_token_id": 150000,
+  "eos_token_id": [
+    150001,
+    150003,
+    150005,
+    150018
+  ],
+  "pad_token_id": 150001,
+  "do_sample": true,
+  "temperature": 1.0,
+  "top_p": 0.95,
+  "top_k": 20
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5ef11a79c57fef7fd870084e5af2fd6f69d0a142d1bfda9f7aeafd205744bbd6
+size 40059474

sglang/README.md

@@ -0,0 +1,202 @@
+# Serving Flint with SGLang
+
+This directory is Flint's serving kit for SGLang 0.5.16: the `flint_sglang` model package (a wheel), a
+Dockerfile, `install.sh` for existing containers, the FA4 learned-sink patch, serve scripts and two
+example clients. The commands below pin a commit of this repository.
+
+Flint reasons at length on hard problems (see `max_tokens` below).
+
+## Quick start (Docker, one GPU)
+
+Requirements:
+- One GPU with ≥141 GB per replica: B300/B200 (Blackwell) or H200 (Hopper). 80 GB GPUs are not supported.
+- NVIDIA driver ≥ 580 (CUDA 13). Older datacenter drivers (e.g. R570) work through the CUDA
+  forward-compatibility libraries in the image (`/usr/local/cuda/compat`), which recent NVIDIA
+  Container Toolkit and enroot setups pick up automatically. Tested: B300 with 580.126.20, H200 with 570.133.20.
+- Building the image needs access to Docker Hub, pypi.org/files.pythonhosted.org, flashinfer.ai and
+  github.com (FlashInfer release assets).
+- About 80 GB of disk for the weights plus about 50 GB for the image.
+
+```bash
+pip install -U huggingface_hub openai
+REV=$(python3 -c "from huggingface_hub import HfApi; print(HfApi().model_info('RecursiveSI/flint').sha)")
+hf download RecursiveSI/flint --revision "$REV" --local-dir "flint-$REV"
+cd "flint-$REV" && hf cache verify RecursiveSI/flint --revision "$REV" --local-dir .   # verify the download
+docker build -t "flint-sglang:${REV:0:12}" sglang
+docker run -d --name flint --gpus '"device=0"' --ipc=host -p 127.0.0.1:30000:30000 \
+  -v "$PWD:/model" -e MODEL=/model "flint-sglang:${REV:0:12}" \
+  bash /model/sglang/serve_b300.sh               # on H200: serve_h200.sh
+until curl -sf localhost:30000/health_generate >/dev/null; do sleep 15; done   # progress: docker logs -f flint
+python3 sglang/example_client.py --base-url http://localhost:30000/v1
+python3 sglang/example_tools.py --base-url http://localhost:30000/v1    # nested tool arguments, parallel calls
+```
+
+The first start takes about 4 minutes on a B300 with the weights on fast local storage (longer when
+many replicas read the weights from network storage at once; budget up to 15 minutes). The log
+shows `Flint weights loaded: 585 parameters matched the checkpoint.` and then
+`The server is fired up and ready to roll!`. `example_client.py` prints the reasoning, a
+`get_weather` call for Paris, and a final answer that uses the tool result (sunny, 21C).
+`example_tools.py` books a meeting and fills a cart through tools with nested argument schemas
+(enums, patterns, bounds, arrays of objects) and makes three parallel weather calls. It answers each
+call with a canned result, which Flint's final answers report.
+
+## Serve scripts
+
+Requests use the model name `flint`. Arguments after the script name are appended to
+`sglang.launch_server`, so a repeated flag overrides the script's value, e.g.
+`bash /model/sglang/serve_b300.sh --mem-fraction-static 0.85`.
+
+| Variable | Default | Notes |
+|---|---|---|
+| `MODEL` | `RecursiveSI/flint` | Use the local download (`/model` in Docker). With the Hub id the server downloads 79.6 GB at start-up. |
+| `REVISION` | unset | Hub commit when `MODEL` is a Hub id (ignored for local paths). |
+| `PORT` | `30000` | Replica port. |
+| `CUDA_VISIBLE_DEVICES` | all GPUs | GPU of a single replica (the server uses the first visible GPU). |
+| `FLINT_TRITON_CACHE_BASE` | `$HOME/.triton/cache` | Kernel cache root; each replica uses its own `flint-<host>-<port>` subdirectory. Mount a volume here to keep compiled kernels across container restarts. |
+
+The servers listen on 0.0.0.0 without authentication: publish ports only on localhost or a private
+network, or append `--api-key <key>` and give clients the same key.
+
+## Validated configurations
+
+One GPU per replica:
+- B300 (x86_64): `serve_b300.sh` (`--attention-backend fa4 --moe-runner-backend flashinfer_trtllm_routed`).
+  B200 and GB200/GB300 (arm64) use the same flags but are not validated yet.
+- H200: `serve_h200.sh` (`--attention-backend fa3 --moe-runner-backend triton`).
+
+Both use `--enable-fp32-lm-head --context-length 262144`. The other settings are those Flint was
+evaluated with on each GPU type (B300: `--mem-fraction-static 0.8 --swa-full-tokens-ratio 0.05`;
+H200: `0.9` / `0.4`).
+
+The model refuses to start with a MoE runner other than `flashinfer_trtllm_routed`, `triton` or
+`auto` (`flashinfer_trtllm` and `triton_kernel` bypass Flint's sigmoid routing), with any
+`--moe-a2a-backend`, with an unpatched FA4 kernel, or if FlashInfer does not use its CUDA RMSNorm
+kernels (`FLASHINFER_USE_CUDA_NORM=1`, which the image and `flint_sglang` set). `--attention-backend
+flashinfer` does not support attention sinks. Tensor, expert or pipeline parallelism, DP attention,
+quantized weights or KV cache (e.g. FP8) and speculative decoding are not validated.
+
+## Several replicas behind a router
+
+Replicas are independent single-GPU servers; the router (`sglang_router` 0.3.2, included in the
+image) spreads requests over them.
+
+```bash
+# 8 replicas on one 8-GPU node, ports 30000-30007 (Blackwell; on H200 add -e SERVE_SCRIPT=serve_h200.sh)
+docker run -d --name flint-8x --gpus all --ipc=host -p 30000-30007:30000-30007 \
+  -v "$PWD:/model" -e MODEL=/model "flint-sglang:${REV:0:12}" bash /model/sglang/serve_node_8x.sh
+# router on any host that can reach every replica
+docker run -d --name flint-router --network host "flint-sglang:${REV:0:12}" \
+  python3 -m sglang_router.launch_router --host 0.0.0.0 --port 8000 --policy cache_aware \
+  --worker-urls $(for n in node0 node1; do for p in $(seq 30000 30007); do printf 'http://%s:%d ' $n $p; done; done)
+```
+
+`serve_node_8x.sh` stops all 8 replicas when one exits; for independent restarts run one container
+per GPU instead (`--gpus '"device=N"'`, `-e PORT=3000N`).
+
+## Existing container (no image build), e.g. enroot/pyxis
+
+Inside a container of the base image pinned in `sglang/Dockerfile` (`lmsysorg/sglang:v0.5.16-cu130`),
+with network access:
+
+```bash
+bash flint-$REV/sglang/install.sh flint-$REV/sglang   # ends with: Flint support installed (flint_sglang-0.1.4-py3-none-any.whl).
+MODEL=$PWD/flint-$REV PORT=30000 bash flint-$REV/sglang/serve_h200.sh   # Blackwell: serve_b300.sh
+```
+
+`install.sh` changes the container in place (FlashInfer 0.6.16.post1, CUTLASS DSL 4.6.2, the patched
+FA4 kernel, `flint_sglang`), can be re-run, and works without root if the container's system Python
+is writable (enroot/pyxis containers are). Keep or save that container (pyxis:
+`--container-save=flint-sglang.sqsh`); a fresh copy of the base image has no Flint support. On H200
+the FA4 patch is applied but unused.
+
+## Capacity and tuning
+
+- KV cache per token: 28 KiB on the 14 full-attention layers (≈7.5 GB for one 262,144-token
+  sequence) plus 70 KiB on the 35 sliding-window layers, which keep only the last 513 tokens of
+  each running request.
+- `--mem-fraction-static` is the share of GPU memory for the weights (79.6 GB) plus the KV pools.
+- `--swa-full-tokens-ratio` sizes the sliding-window pool relative to the full-attention pool:
+  raise it for many concurrent short requests, lower it for few long ones.
+
+## Chat format and API usage
+
+Tool definitions appear after leading system messages, before the first developer or other role.
+
+- Use `/v1/chat/completions` with the parsers and grammar backend in the serve scripts
+  (`--tool-call-parser flint --reasoning-parser flint --grammar-backend flint`). Flint refuses to
+  start without `--grammar-backend flint` and `--reasoning-parser flint`, or with
+  `--enable-strict-thinking` or `--constrained-json-disable-any-whitespace`, which that backend
+  doesn't support. Without a tool-call parser, tool calls stay as literal `<tool_call>` text in
+  `content`.
+- Tool-call grammar: `--tool-call-parser flint` parses Flint's tool calls and constrains every
+  request with `tools` (unless `tool_choice` is `none`) with XGrammar to Flint's format: an optional
+  `<think>…</think>` (from `--grammar-backend flint`), text without protocol markers, then tool calls
+  to the request's tools: any number with `tool_choice` `auto`, at least one with `required`, exactly
+  one call of the named function with a named choice. As in OpenAI's API, a tool's arguments must
+  match its JSON schema only when the tool is sent with `"strict": true`; they are then also written
+  in the order the tool declares its parameters (list optional parameters where they belong). Follow
+  OpenAI's strict-mode rules: give every object `properties` or `additionalProperties` and every array
+  `items` (otherwise XGrammar accepts no value for the object and only `[]` for the array); a strict
+  schema XGrammar can't compile (for example a pattern with a lookahead) fails the request. Other
+  tools keep the call format but not their schema.
+  `parallel_tool_calls: false` allows a single call. The turn still ends with Flint's stop tokens.
+- Tool-call limits:
+  - A strict string value (an enum, `const`, `pattern`, `format` or `maxLength`) written with whitespace
+    around it fails its schema: XGrammar allows the whitespace and the parser keeps it. Flint rarely
+    does this; it has been seen on a prompt that asks it to print a tool call as text.
+  - A string argument can't contain the literal text `</tool_call>`. The format has no escaping, so the
+    parser ends the call there and reads the rest as output: text Flint copies into an argument can
+    add a call, without arguments, to a listed tool.
+  - When streaming, a call whose last argument is a JSON object can arrive without its final `}`.
+    SGLang adds it back only for the response's last call.
+  - With `tool_choice: "none"` nothing is constrained, so call markup Flint writes can reach `content`.
+- Structured output: `response_format` (`json_schema`, `json_object` or `structural_tag`) and SGLang's
+  `regex` and `ebnf` parameters constrain `content`. Flint may first reason in `<think>…</think>`
+  (returned in `reasoning_content`), and the constraint applies to the answer after it. A request with
+  both `tools` and a response format gets only the response format, and `<tool_call>` text in the
+  answer is still read as a tool call. Use `/v1/chat/completions`: `/generate` and `/v1/completions`
+  return Flint's raw text, including any `<think>…</think>` written before a constrained answer.
+- Sampling: temperature 1.0, top_p 0.95, top_k 20. These are the `generation_config.json` defaults
+  the server applies when a request omits them; OpenAI clients pass `top_k` via `extra_body`.
+  Greedy decoding is not validated.
+- Reasoning: the model decides on each turn whether to reason in `<think>…</think>` (there is no
+  switch or budget). Reasoning is returned in `reasoning_content` and counts toward `max_tokens`:
+  on GPQA-Diamond it used a median of 4.3K tokens (p95 33K, p99 46K; about 5% of answers exceed 32K).
+  Use `max_tokens` of at least 32768 for hard problems (Flint was evaluated with up to 81920); `finish_reason: "length"` means the
+  turn was cut off, possibly before any content or tool call. Keep `reasoning_content` on assistant
+  messages when you send the conversation back. `thinking` / `enable_thinking` in
+  `chat_template_kwargs` have no effect: the `flint` reasoning parser reads `<think>…</think>` when
+  Flint writes it and never assumes a turn starts in reasoning, so turns without reasoning keep their
+  content and their tool-call grammar.
+- Messages: content must be a string (flatten content-part arrays). A `developer` message uses
+  its own `<|developer|>` role marker (token ID 150018). There is no default system prompt. As in
+  training, the template removes NUL characters and `<think>`/`</think>` from system, developer,
+  user and tool text and escapes role markers there (`<|user|>` becomes `&lt;|user|&gt;`). It does
+  not escape the tool tags (`<tool_call>`, `<tool_response>`, `<arg_key>`, `<arg_value>`); strip
+  those from untrusted tool output.
+- Tools: send `tools` with every request. Each function definition is rendered as the template
+  receives it; through the server that is `description`, `name`, `parameters` and `strict` (other
+  fields are dropped). Send `function.arguments` back unchanged (the JSON string you received) and
+  one `tool` message per call, in the order of `tool_calls`. When calling `apply_chat_template`
+  directly, decode argument strings into dictionaries first.
+- Generation stops at `<|end_of_text|>`, `<|user|>`, `<|observation|>` or `<|developer|>`
+  (ids 150001, 150003, 150005, 150018). No BOS token is used for chat.
+
+## Versions and verification
+
+- Quote the full commit SHA (`$REV`) in results and bug reports. Download each commit into a new
+  directory (`hf download` does not delete files removed upstream).
+- Serving kit: SGLang 0.5.16 (base image pinned by digest in `sglang/Dockerfile`; includes
+  transformers 5.12.1, torch 2.11.0+cu130, sglang-router 0.3.2), `flint_sglang` 0.1.4, FlashInfer
+  0.6.16.post1, nvidia-cutlass-dsl 4.6.2, quack-kernels 0.6.4, apache-tvm-ffi 0.1.12.
+
+When reporting a problem, include the commit SHA, the image ID (`docker inspect --format '{{.Id}}'
+flint-sglang:<tag>`), `nvidia-smi` output, the launch command including extra arguments, the start-up
+log and, for output problems, the request JSON and the raw response.
+
+## Transformers
+
+The tokenizer and chat template load with `AutoTokenizer.from_pretrained`; decode each
+`tool_calls[].function.arguments` string into a dict before `apply_chat_template`. Transformers does
+not support Flint yet, so `config.json` loads with `FlintConfig.from_pretrained` from
+`flint_sglang.configuration_flint` (transformers ≥ 5.8, tested with 5.12.1) rather than `AutoConfig`.

sglang/serve_h200.sh

@@ -0,0 +1,38 @@
+#!/usr/bin/env bash
+# One Flint replica on one Hopper H200 GPU (141 GB). FA4 and the trtllm MoE kernels are
+# Blackwell-only, so this uses FA3 attention and Triton MoE. 80 GB H100s cannot hold the BF16
+# weights on one GPU.
+#
+#   MODEL=/path/to/flint CUDA_VISIBLE_DEVICES=0 PORT=30000 bash serve_h200.sh
+#
+# Extra arguments are passed through to sglang.launch_server.
+set -euo pipefail
+
+MODEL="${MODEL:-RecursiveSI/flint}"
+PORT="${PORT:-30000}"
+revision_args=()
+if [[ -n "${REVISION:-}" ]]; then
+  revision_args=(--revision "$REVISION")
+fi
+
+export SGLANG_EXTERNAL_MODEL_PACKAGE="${SGLANG_EXTERNAL_MODEL_PACKAGE:-flint_sglang}"
+export FLASHINFER_USE_CUDA_NORM="${FLASHINFER_USE_CUDA_NORM:-1}"
+# One Triton cache per replica: replicas compiling the same kernels into one cache directory can race.
+export TRITON_CACHE_DIR="${FLINT_TRITON_CACHE_BASE:-${HOME:-/tmp}/.triton/cache}/flint-$(hostname)-${PORT}"
+
+exec python3 -m sglang.launch_server \
+  --model-path "$MODEL" "${revision_args[@]}" \
+  --served-model-name flint \
+  --host 0.0.0.0 --port "$PORT" \
+  --tp-size 1 \
+  --attention-backend fa3 \
+  --moe-runner-backend triton \
+  --enable-fp32-lm-head \
+  --context-length 262144 \
+  --chunked-prefill-size 4096 \
+  --swa-full-tokens-ratio 0.4 \
+  --mem-fraction-static 0.9 \
+  --tool-call-parser flint \
+  --reasoning-parser flint \
+  --grammar-backend flint \
+  "$@"

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a263cce0b7e2b03f7ee07ce809783ef4b47f160c3f268a4ea1524f638da45b68
+size 10879128

tokenizer_config.json

@@ -0,0 +1,1295 @@
+{
+  "add_bos_token": false,
+  "add_eos_token": false,
+  "added_tokens_decoder": {
+    "150000": {
+      "content": "<|begin_of_text|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "150001": {
+      "content": "<|end_of_text|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "150002": {
+      "content": "<|system|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "150003": {
+      "content": "<|user|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "150004": {
+      "content": "<|assistant|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "150005": {
+      "content": "<|observation|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "150006": {
+      "content": "<think>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "150007": {
+      "content": "</think>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "150008": {
+      "content": "<tools>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "150009": {
+      "content": "</tools>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "150010": {
+      "content": "<tool_call>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "150011": {
+      "content": "</tool_call>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "150012": {
+      "content": "<arg_key>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "150013": {
+      "content": "</arg_key>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "150014": {
+      "content": "<arg_value>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "150015": {
+      "content": "</arg_value>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "150016": {
+      "content": "<tool_response>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "150017": {
+      "content": "</tool_response>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "150018": {
+      "content": "<|developer|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "150019": {
+      "content": "<|reserved_150019|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "150020": {
+      "content": "<|reserved_150020|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "150021": {
+      "content": "<|reserved_150021|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "150022": {
+      "content": "<|reserved_150022|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "150023": {
+      "content": "<|reserved_150023|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "150024": {
+      "content": "<|reserved_150024|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "150025": {
+      "content": "<|reserved_150025|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "150026": {
+      "content": "<|reserved_150026|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "150027": {
+      "content": "<|reserved_150027|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "150028": {
+      "content": "<|reserved_150028|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "150029": {
+      "content": "<|reserved_150029|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "150030": {
+      "content": "<|reserved_150030|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "150031": {
+      "content": "<|reserved_150031|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "150032": {
+      "content": "<|reserved_150032|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "150033": {
+      "content": "<|reserved_150033|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "150034": {
+      "content": "<|reserved_150034|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "150035": {
+      "content": "<|reserved_150035|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "150036": {
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+}