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Model: seanpoyner/smolcode-coder-orchestrate-1.5b-tools
Source: Original Platform

.gitattributes

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+smolcode-coder-orchestrate-1.5b-q4_k_m.gguf filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
+tags:
+- code
+- function-calling
+- tool-use
+- agent
+- small-language-model
+datasets:
+- NousResearch/hermes-function-calling-v1
+language:
+- en
+pipeline_tag: text-generation
+---
+
+# smolcode-coder-1.5b-tools
+
+A LoRA fine-tune of **Qwen2.5-Coder-1.5B-Instruct** that teaches the model to emit
+**native `<tool_call>` function calls**, so a 1.5B *coder* model can actually drive an
+agentic write → run → fix → verify loop.
+
+Built for [**smolcode**](https://gitea.poyner.ai/sean/smolcode) — an SLM-optimized
+agentic coding assistant — for the Hugging Face **Build Small** hackathon.
+
+## Why
+Out of the box, small Qwen-Coder models describe tool calls as plain-text/```json
+instead of emitting the native `<tool_call>` token (id 151657) that runtimes (Ollama,
+llama.cpp) parse into OpenAI-style `tool_calls` — which breaks agentic loops. This
+fine-tune closes that gap on a tiny (1.5B) model: **100% native `<tool_call>` emission**
+in free generation on held-out prompts (base model: 0%).
+
+## Results
+- **Native tool-call rate:** 100% (16/16 held-out prompts) — the release gate.
+- **Agentic bench (smolcode pass@1, 10 tasks):** 9/10 as the entry tier of a
+  1.5B→8B→30B ladder, solving **7/10 entirely on its own** (2–16s each). For
+  comparison the all-Granite ladder (3B entry) scores 10/10 — the 1.5B carries the
+  same standalone load as a 2×-larger 3B.
+- **Train loss:** 0.138 (3 epochs, assistant-only loss).
+
+## Training
+- **Base:** Qwen/Qwen2.5-Coder-1.5B-Instruct
+- **Method:** bf16 LoRA (r=16, α=32) on attention + MLP projections, **plus full
+  training of `embed_tokens` + `lm_head`** (`modules_to_save`) — required so the model
+  can *output* the `<tool_call>` special token, which LoRA on attention/MLP alone
+  cannot. **Assistant-only loss** (loss on tool calls + final answers only).
+- **Data:** NousResearch/hermes-function-calling-v1 (breadth) + synthetic smolcode
+  tool-use trajectories (sharpness), all rendered through the *same*
+  `apply_chat_template(tools=...)` used at inference — training target is byte-identical
+  to the served prompt (fixes the v1 train/inference template mismatch).
+- **Schedule:** 3 epochs, full 2048 sequence length. Trained on Modal (A100).
+
+## Serving — read this, two non-obvious requirements
+1. **Serve via the GGUF, not the safetensors directly.** Ollama's bf16-safetensors
+   auto-import produces garbage (`??????`) for this model. Use the included
+   `smolcode-1.5b-q4_k_m.gguf` (converted with llama.cpp `convert_hf_to_gguf.py`):
+   ```bash
+   ollama create smolcode-coder-1.5b:tools -f Modelfile   # Modelfile is in this repo
+   ```
+2. **`repeat_penalty` / `repetition_penalty` MUST be 1.0.** The tool system prompt
+   literally contains the `<tool_call>` token, so any penalty > 1 suppresses the model
+   from emitting it (you'll see a stray token + bare JSON instead). The included
+   `Modelfile` sets `PARAMETER repeat_penalty 1.0`. For raw `transformers.generate`,
+   pass `repetition_penalty=1.0`.
+
+With those, Ollama's `/v1/chat/completions` returns proper native `tool_calls`.
+
+## Use (transformers)
+Standard Qwen2.5 chat template with `tools=`; greedy, `repetition_penalty=1.0`. The
+model responds with `<tool_call>{"name": ..., "arguments": ...}</tool_call>`.
+
+## Files
+- `model.safetensors` + tokenizer/config — the merged model (lm_head untied).
+- `smolcode-1.5b-q4_k_m.gguf` — quantized GGUF for serving.
+- `Modelfile` — Ollama import recipe (template + `repeat_penalty 1.0`).
+
+## License
+Apache-2.0 (inherits from the base model).

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 1536,
+  "initializer_range": 0.02,
+  "intermediate_size": 8960,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "qwen2",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 2,
+  "pad_token_id": null,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000.0,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "transformers_version": "5.12.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.1,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "5.12.0"
+}

model.safetensors

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+size 3554214752

smolcode-coder-orchestrate-1.5b-q4_k_m.gguf

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tokenizer.json

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tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "is_local": false,
+  "local_files_only": false,
+  "model_max_length": 32768,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}