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Model: Keitsuna123/llama-3.2-1b-fc-dpo Source: Original Platform
2026-07-20 21:43:10 +08:00
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--- a/README.md
+++ b/README.md
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+---
+base_model: meta-llama/Llama-3.2-1B-Instruct
+library_name: transformers
+tags:
+- function-calling
+- tool-use
+- llama
+- dpo
+- preference-optimization
+- bfcl
+license: llama3.2
+language:
+- en
+---
+
+# Llama-3.2-1B-Instruct — Function Calling (DPO)
+
+The [v1 SFT model](https://huggingface.co/Keitsuna123/llama-3.2-1b-fc-sft-full) further trained with **DPO (Direct Preference Optimization)** to recover an irrelevance-detection regression. DPO is a *preference optimization* method — not reinforcement learning — that optimizes a model to prefer "chosen" over "rejected" responses directly, without a reward model or sampling. This is one of six models in a study comparing supervised, preference, and RL post-training for small-model function calling.
+
+**Code & writeup:** https://github.com/keitake123/llama-function-calling-study
+
+## Results (BFCL v4)
+
+| Category | Base | v1 SFT | GRPO (RL) | DPO (pref.) | Path B (SFT) |
+|---|---|---|---|---|---|
+| irrelevance | 35.8 | 5.8 | 5.4 | **8.3** | 21.7 |
+| live_irrelevance | 67.3 | 16.9 | 17.2 | **28.6** | 36.8 |
+| simple_python | 75.0 | 77.5 | 77.2 | 77.5 | 78.2 |
+| multiple | 50.5 | 74.0 | 74.0 | 73.5 | 72.0 |
+| live_simple | 31.8 | 57.0 | 56.2 | 58.5 | 54.3 |
+| live_multiple | 7.3 | 38.8 | 39.4 | 37.0 | 35.7 |
+| live_relevance | 43.8 | 93.8 | 93.8 | 81.2 | 81.2 |
+
+**Finding:** DPO produced **partial** irrelevance recovery (5.8→8.3 non-live; 16.9→28.6 live) — clearly better than GRPO (which showed no recovery) but below refusal-SFT. This fits a clean pattern across the three methods:
+
+> Recovery scaled with how *directly* each method exposed the model to the target behavior.
+> **GRPO** never samples a refusal (the SFT always-call prior is too strong) → no signal → no recovery.
+> **DPO** is *shown* refusals as the "chosen" response → partial recovery, at low cost to call categories.
+> **Refusal-SFT** trains *directly* on refusals → strongest recovery.
+>
+> Ordering: imitation (SFT) > preference (DPO) > RL (GRPO). RL recovers least because it cannot reinforce a behavior absent from the policy's sampling distribution.
+
+Notably, DPO preserved call-required categories well (simple_python, live_simple held up), giving arguably the best precision/recall balance despite a lower raw irrelevance score than Path B.
+
+## Training details
+
+- **Base:** v1 SFT model, cleanly merged (see note below)
+- **Method:** DPO (TRL `DPOTrainer`), LoRA (r=16, α=32)
+- **Data:** ~1,600 preference pairs, 50/50 mix:
+  - *Irrelevance pairs:* chosen = natural refusal, rejected = v1's hallucinated call (generated by running v1 on the irrelevance prompts)
+  - *Call-required pairs:* chosen = correct call, rejected = wrong-function call
+- **Config:** 1 epoch, effective batch 8, lr 5e-6, **beta (KL) 0.3**, bf16
+- **Hardware:** 1× RTX A6000
+
+**Note on stability:** an initial run with beta=0.1 and a stacked (unmerged) reference adapter caused output-format drift (the model emitted a malformed call schema and occasionally code). Fixed by (1) cleanly merging the v1 adapter into the base before DPO so the reference model is well-formed, and (2) raising beta to 0.3 for a stronger anchor. See the repo's LESSONS.md.
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("Keitsuna123/llama-3.2-1b-fc-dpo")
+model = AutoModelForCausalLM.from_pretrained(
+    "Keitsuna123/llama-3.2-1b-fc-dpo", torch_dtype=torch.bfloat16, device_map="auto"
+)
+
+messages = [{"role": "user", "content": "What's the weather in Tokyo?"}]
+tools = [{"type": "function", "function": {
+    "name": "get_weather", "description": "Get the weather for a location",
+    "parameters": {"type": "object", "properties": {"location": {"type": "string"}}, "required": ["location"]}
+}}]
+
+text = tokenizer.apply_chat_template(messages, tools=tools, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+out = model.generate(**inputs, max_new_tokens=128, do_sample=False)
+print(tokenizer.decode(out[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True))
+```
+
+## Limitations
+
+- Residual irrelevance failures concentrate in short, factual-seeming queries ("what's 2+2", "capital of France") — the same sub-case that beat every method in this study.
+- Parallel calls near-zero (single-call training only).
+
+## Part of a series
+
+| Model | Method | Description |
+|---|---|---|
+| [fc-sft-full](https://huggingface.co/Keitsuna123/llama-3.2-1b-fc-sft-full) | SFT | v1 SFT on xLAM |
+| [fc-sft-v2-merged](https://huggingface.co/Keitsuna123/llama-3.2-1b-fc-sft-v2-merged) | SFT | + distilabel (data-scaling ablation) |
+| [fc-grpo](https://huggingface.co/Keitsuna123/llama-3.2-1b-fc-grpo) | RL | GRPO (no recovery) |
+| [fc-pathb](https://huggingface.co/Keitsuna123/llama-3.2-1b-fc-pathb) | SFT | refusal-SFT (best recovery) |
+| [fc-dpo](https://huggingface.co/Keitsuna123/llama-3.2-1b-fc-dpo) | DPO | preference optimization (this model) |
+
+## References & Acknowledgements
+
+- **Base model:** Llama 3.2 (Meta AI) — [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct)
+- **Training data:** Salesforce xLAM — [xlam-function-calling-60k](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k); preference pairs constructed for this study
+- **Method:** DPO (Rafailov et al., "Direct Preference Optimization")
+- **Benchmark:** Berkeley Function Calling Leaderboard (BFCL) — [Gorilla project](https://github.com/ShishirPatil/gorilla)
+- **Frameworks:** HuggingFace TRL (DPOTrainer), PEFT, Transformers
+
+## Citation
+
+```bibtex
+@misc{taketsuna2026_fc_smallmodel,
+  title  = {Small-Model Function Calling: Comparing SFT, Data Scaling, GRPO, and DPO Post-Training},
+  author = {Taketsuna, Keiichi},
+  year   = {2026},
+  howpublished = {\url{https://github.com/keitake123/llama-function-calling-study}},
+  note   = {Llama-3.2-1B function-calling post-training study on BFCL v4}
+}
+```
--- a/chat_template.jinja
+++ b/chat_template.jinja
@@ -0,0 +1,93 @@
+{{- bos_token }}
+{%- if custom_tools is defined %}
+    {%- set tools = custom_tools %}
+{%- endif %}
+{%- if not tools_in_user_message is defined %}
+    {%- set tools_in_user_message = true %}
+{%- endif %}
+{%- if not date_string is defined %}
+    {%- if strftime_now is defined %}
+        {%- set date_string = strftime_now("%d %b %Y") %}
+    {%- else %}
+        {%- set date_string = "26 Jul 2024" %}
+    {%- endif %}
+{%- endif %}
+{%- if not tools is defined %}
+    {%- set tools = none %}
+{%- endif %}
+
+{#- This block extracts the system message, so we can slot it into the right place. #}
+{%- if messages[0]['role'] == 'system' %}
+    {%- set system_message = messages[0]['content']|trim %}
+    {%- set messages = messages[1:] %}
+{%- else %}
+    {%- set system_message = "" %}
+{%- endif %}
+
+{#- System message #}
+{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
+{%- if tools is not none %}
+    {{- "Environment: ipython\n" }}
+{%- endif %}
+{{- "Cutting Knowledge Date: December 2023\n" }}
+{{- "Today Date: " + date_string + "\n\n" }}
+{%- if tools is not none and not tools_in_user_message %}
+    {{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+{%- endif %}
+{{- system_message }}
+{{- "<|eot_id|>" }}
+
+{#- Custom tools are passed in a user message with some extra guidance #}
+{%- if tools_in_user_message and not tools is none %}
+    {#- Extract the first user message so we can plug it in here #}
+    {%- if messages | length != 0 %}
+        {%- set first_user_message = messages[0]['content']|trim %}
+        {%- set messages = messages[1:] %}
+    {%- else %}
+        {{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
+{%- endif %}
+    {{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
+    {{- "Given the following functions, please respond with a JSON for a function call " }}
+    {{- "with its proper arguments that best answers the given prompt.\n\n" }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+    {{- first_user_message + "<|eot_id|>"}}
+{%- endif %}
+
+{%- for message in messages %}
+    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
+        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
+    {%- elif 'tool_calls' in message %}
+        {%- if not message.tool_calls|length == 1 %}
+            {{- raise_exception("This model only supports single tool-calls at once!") }}
+        {%- endif %}
+        {%- set tool_call = message.tool_calls[0].function %}
+        {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
+        {{- '{"name": "' + tool_call.name + '", ' }}
+        {{- '"parameters": ' }}
+        {{- tool_call.arguments | tojson }}
+        {{- "}" }}
+        {{- "<|eot_id|>" }}
+    {%- elif message.role == "tool" or message.role == "ipython" %}
+        {{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
+        {%- if message.content is mapping or message.content is iterable %}
+            {{- message.content | tojson }}
+        {%- else %}
+            {{- message.content }}
+        {%- endif %}
+        {{- "<|eot_id|>" }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
+{%- endif %}
--- a/config.json
+++ b/config.json
@@ -0,0 +1,40 @@
+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "dtype": "bfloat16",
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 16,
+  "num_key_value_heads": 8,
+  "pad_token_id": null,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_parameters": {
+    "factor": 32.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_theta": 500000.0,
+    "rope_type": "llama3"
+  },
+  "tie_word_embeddings": true,
+  "transformers_version": "5.13.0",
+  "use_cache": true,
+  "vocab_size": 128256
+}
--- a/generation_config.json
+++ b/generation_config.json
@@ -0,0 +1,12 @@
+{
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "5.13.0"
+}
--- a/model.safetensors
+++ b/model.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:47055c3256bc01b347c1bd1fd00753745a5fe1741f2a3ef52d2e42f5a6c98892
+size 2471645608
--- a/tokenizer.json
+++ b/tokenizer.json
--- a/tokenizer_config.json
+++ b/tokenizer_config.json
@@ -0,0 +1,15 @@
+{
+  "backend": "tokenizers",
+  "bos_token": "<|begin_of_text|>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|eot_id|>",
+  "is_local": false,
+  "local_files_only": false,
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 131072,
+  "pad_token": "<|eot_id|>",
+  "tokenizer_class": "TokenizersBackend"
+}