Publish Mossez-100M-Coder-Instruct

Verified FP32 Safetensors release with tokenizer, model card, dataset attribution, training report, evaluation, and SHA256SUMS.

DATASET_ATTRIBUTION.md

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+# Dataset attribution
+
+The Coder-Instruct v1 SFT corpus is deterministic project-authored material
+licensed under Apache-2.0. It imports no external instruction dataset, private
+data, chat exports, Telegram data, or chain-of-thought material.
+
+## Composition
+
+- Train: 2,640 examples, 323,960 rendered tokens, 88 source groups.
+- Validation: 330 examples, 40,504 rendered tokens, 11 source groups.
+- Test: 330 examples, 40,518 rendered tokens, 11 source groups.
+- Maximum rendered sequence: 149 tokens; model limit: 1,024.
+- Eleven balanced task types: short function, completion, explanation, bug fix,
+  refactor, unit test, traceback, JSON/YAML conversion, SQL, PowerShell, and FIM repair.
+- Source groups are disjoint across train, validation, and test.
+
+## Gates
+
+License/provenance, secret, email-like PII, exact deduplication, cross-split
+source-group leakage, chain-of-thought exclusion, and chat-marker collision
+gates passed. Bounded local validators passed for Python AST/fragments, JSON,
+YAML, SQL, safe PowerShell, and concise text.
+
+This conservative, template-heavy corpus validates the SFT pipeline and balanced
+held-out evaluation. It does not establish broad coding-assistant competence.

EVALUATION.md

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+# Evaluation
+
+Evaluation used frozen, source-group-disjoint validation and test splits with
+assistant-only loss. Lower is better.
+
+| Model | Validation loss | Test loss |
+|---|---:|---:|
+| Source Coder-Base | 2.008803 | 2.023115 |
+| One-epoch Coder-Instruct | 0.029855 | 0.032257 |
+
+## Per-task loss
+
+| Task | Source val | Candidate val | Source test | Candidate test |
+|---|---:|---:|---:|---:|
+| bug_fix | 0.666634 | 0.002418 | 0.685901 | 0.002504 |
+| completion | 1.098412 | 0.072219 | 1.184431 | 0.074582 |
+| explain | 3.757432 | 0.003248 | 3.861036 | 0.003465 |
+| fim_repair | 2.926159 | 0.171918 | 2.934412 | 0.188182 |
+| json_yaml_conversion | 1.304223 | 0.002704 | 1.310993 | 0.002706 |
+| refactor | 1.088105 | 0.002698 | 1.089293 | 0.002557 |
+| shell | 2.545998 | 0.001775 | 2.548895 | 0.001855 |
+| short_function | 1.503674 | 0.003699 | 1.520041 | 0.003951 |
+| sql | 2.761549 | 0.018859 | 2.715693 | 0.021215 |
+| traceback | 3.734223 | 0.002768 | 3.769516 | 0.002884 |
+| unit_test | 1.751150 | 0.075700 | 1.721415 | 0.083690 |
+
+Overall and all 11 per-task losses improved against both the source and the
+100-step SFT pilot. A deterministic 11-prompt generation smoke passed all
+local syntax/format/safety validators; 9 outputs exactly matched references.
+
+These results come from a small, project-authored, template-heavy corpus.
+They do not establish performance on HumanEval, MBPP, SWE-bench, security
+tasks, repository-level work, or arbitrary real-world prompts.

LICENSE

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NOTICE.md

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+# Notice
+
+Mossez-100M-Coder-Instruct is an experimental derivative in the Mossez-100M family.
+
+This model is derived from Mossez-100M-Coder-Base through assistant-only SFT on deterministic project-authored Apache-2.0 examples. The general Mossez-100M-Instruct supplied only tokenizer/chat-template/release references, not source weights.
+
+Copyright 2026 Mossez Systems. Licensed under Apache-2.0. This research model is
+not production-ready; generated code must be reviewed, tested, and sandboxed.

README.md

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+---
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+base_model:
+- mossez-systems/Mossez-100M-Coder-Base
+tags:
+- causal-lm
+- conversational
+- code
+- fill-in-the-middle
+- instruct
+- llama
+- research
+- experimental
+---
+
+# Mossez-100M-Coder-Instruct
+
+Mossez-100M-Coder-Instruct is an experimental 100M-parameter coding instruction
+model with this weight lineage:
+
+`Mossez-100M-Base -> Mossez-100M-Coder-Base -> Mossez-100M-Coder-Instruct`.
+
+The general [`Mossez-100M-Instruct`](https://huggingface.co/mossez-systems/Mossez-100M-Instruct)
+was used only as a tokenizer, chat-template, release, and inference reference;
+its weights were not used as source weights for this model.
+
+## Model details
+
+| Property | Value |
+|---|---:|
+| Parameters | 100,098,048 |
+| Architecture | Llama-compatible decoder-only Transformer |
+| Layers / hidden size | 12 / 768 |
+| Query / KV heads | 12 / 4 |
+| Context length | 1,024 tokens |
+| Vocabulary | 32,007 |
+| Objective | Assistant-only SFT loss |
+| Weight format | Safetensors, FP32 |
+| License | Apache-2.0 |
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "mossez-systems/Mossez-100M-Coder-Instruct"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
+
+messages = [{"role": "user", "content": "Write a short Python function that adds two integers."}]
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+output = model.generate(**inputs, do_sample=False, max_new_tokens=96)
+new_tokens = output[0, inputs.input_ids.shape[1]:]
+print(tokenizer.decode(new_tokens, skip_special_tokens=True))
+```
+
+## Training and evaluation
+
+The model was fine-tuned for one bounded epoch: 660 optimizer steps over 2,640
+project-authored examples, using assistant-only loss. Immutable validation and
+test sets contain 330 examples each across 11 balanced task types. See
+[TRAINING_REPORT.md](TRAINING_REPORT.md), [EVALUATION.md](EVALUATION.md), and
+[DATASET_ATTRIBUTION.md](DATASET_ATTRIBUTION.md).
+
+The released `model.safetensors` SHA-256 is
+`0aade7d070122633abccd70cc5500e5bc36c7f69fafeadd9f5ee0b5a3e0766bf`.
+
+## Limitations
+
+This is a small research model, not a reliable or safe production coding
+assistant. The authored SFT corpus is balanced but narrow and template-heavy,
+so held-out loss may overstate general-world capability. Expect repetition,
+incorrect constants, malformed code, hallucinated APIs, weak instruction
+following, and early EOS. Validate, test, and sandbox every output.

SHA256SUMS

@@ -0,0 +1,13 @@
+b5eb03b52ce8941d22739e41bae8ec4d1ab2a529d8abecb42c6f68430616a161  chat_template.jinja
+aae190b1daeab09fec8ab276b2a97bb60ef56773c44db361ad53fd4c594b7017  config.json
+def1e269f57ae94c4bc348b85bef347f176bfc4d295de12ec9ee155739de2c61  DATASET_ATTRIBUTION.md
+b0ae233b7e29a39cef6c6cc055ddbad35e5b38fa92fb3577e0ece623d2ae8569  EVALUATION.md
+0931ff2f000839227285e26dba99974b2383f6fdd1acffd9cfacf971b22df213  generation_config.json
+8173d5c29b4f956d532781d2b86e4e30f83e6b7878dce18c919451d6ba707c90  LICENSE
+0aade7d070122633abccd70cc5500e5bc36c7f69fafeadd9f5ee0b5a3e0766bf  model.safetensors
+99669f6f98c26b057f81967e442d1e2ae7f0e3f6fa1999c8a3d2f043a735a25d  NOTICE.md
+b978cd04f3725a48c8b9f934f46159ae69e8c82728e9129236da0131c5c55664  README.md
+813c2723c694d037427269cca895d01641f930d6a8f269ce7bf8e1e16e24dabe  special_tokens_map.json
+e9551d84b9947f741763bf815a2d5f6bfcc47a3b67c73fcbf386223e8ed969be  tokenizer.json
+32c07b84f72b62dad43bc3956c541bfbc7b6ac1039930a7e1b8fd2a61fbd2ae9  tokenizer_config.json
+90bd11e7d3ec83e90acb4b5bad9c0fdc3f92a160b5970a2b93979649c141ef0f  TRAINING_REPORT.md

TRAINING_REPORT.md

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+# Training report
+
+## Lineage
+
+- Source weights: the selected one-epoch Mossez-100M-Coder-Base.
+- Source model SHA-256: `aba529bf10ad9f3acb5294c8bc2b4c93d20d25c6cff3a235a8659503b9ac1837`.
+- Final model SHA-256: `0aade7d070122633abccd70cc5500e5bc36c7f69fafeadd9f5ee0b5a3e0766bf`.
+- The general Mossez-100M-Instruct supplied no source weights.
+
+## Run
+
+- Objective: assistant-only supervised fine-tuning.
+- One bounded epoch: 660 contiguous finite optimizer steps.
+- Examples: 2,640 train; 330 validation; 330 test.
+- Rendered training tokens: 323,960; assistant target tokens: 105,100.
+- Precision: BF16; fused AdamW; micro-batch 1; gradient accumulation 4.
+- Validation loss: 1.991049 at step 0 to 0.031888 at step 660.
+- Early stopping: not triggered; validation regression count: 0.
+- Final checkpoint: best validation checkpoint, step 660.
+
+## Preflight and integrity
+
+- Assistant-only masks were asserted at corpus load and before each forward pass.
+- A real CUDA memory probe passed through micro-batch 8; training used micro-batch 1.
+- A 10-step smoke passed, followed by a bit-exact cross-process resume test.
+- An independent 100-step pilot improved overall and all per-task held-out losses.
+- Corpus manifest SHA-256: `aca68b911788a1a7d93671bc27835b3d279cd612c02e8e3add91e2d3b2079084`.
+- Tokenizer manifest SHA-256: `f24c2e10522e866a889177e3a2258d84f1d8a863d784894a72974e391c1abdec`.

chat_template.jinja

@@ -0,0 +1,5 @@
+{% for message in messages %}{{ "<|" ~ message.role ~ "|>
+" ~ message.content ~ "
+<|end|>
+" }}{% endfor %}{% if add_generation_prompt %}{{ "<|assistant|>
+" }}{% endif %}

config.json

@@ -0,0 +1,30 @@
+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "dtype": "float32",
+  "eos_token_id": 2,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 2048,
+  "max_position_embeddings": 1024,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "num_key_value_heads": 4,
+  "pad_token_id": 3,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "tie_word_embeddings": true,
+  "transformers_version": "4.56.2",
+  "use_cache": true,
+  "vocab_size": 32007,
+  "rope_scaling": null,
+  "rope_theta": 10000.0
+}

generation_config.json

@@ -0,0 +1,12 @@
+{
+  "bos_token_id": 1,
+  "eos_token_id": [
+    2,
+    32003
+  ],
+  "pad_token_id": 3,
+  "transformers_version": "4.56.2",
+  "use_cache": true,
+  "do_sample": false,
+  "max_new_tokens": 160
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0aade7d070122633abccd70cc5500e5bc36c7f69fafeadd9f5ee0b5a3e0766bf
+size 400404416

special_tokens_map.json

@@ -0,0 +1,15 @@
+{
+  "additional_special_tokens": [
+    {"content": "<|system|>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false},
+    {"content": "<|user|>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false},
+    {"content": "<|assistant|>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false},
+    {"content": "<|end|>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false},
+    {"content": "<|fim_prefix|>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false},
+    {"content": "<|fim_middle|>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false},
+    {"content": "<|fim_suffix|>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false}
+  ],
+  "bos_token": {"content": "<s>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false},
+  "eos_token": {"content": "</s>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false},
+  "pad_token": {"content": "<pad>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false},
+  "unk_token": {"content": "<unk>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false}
+}

tokenizer_config.json

@@ -0,0 +1,23 @@
+{
+  "additional_special_tokens": [
+    "<|system|>",
+    "<|user|>",
+    "<|assistant|>",
+    "<|end|>",
+    "<|fim_prefix|>",
+    "<|fim_middle|>",
+    "<|fim_suffix|>"
+  ],
+  "backend": "tokenizers",
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 1024,
+  "pad_token": "<pad>",
+  "tokenizer_class": "PreTrainedTokenizerFast",
+  "unk_token": "<unk>"
+}