From 8d27ef5d7c1adbc77c4a3d656db6003eb6bd8b5f Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Mon, 7 Sep 2026 01:31:14 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E5=8C=96=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E7=94=B1ModelHub=20XC=E7=A4=BE=E5=8C=BA=E6=8F=90?= =?UTF-8?q?=E4=BE=9B=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Model: jackf857/Llama-3.2-1B-Instruct-DPO-HH Source: Original Platform --- .gitattributes | 36 ++++++++++ README.md | 156 +++++++++++++++++++++++++++++++++++++++++ chat_template.jinja | 93 ++++++++++++++++++++++++ config.json | 40 +++++++++++ generation_config.json | 12 ++++ model.safetensors | 3 + tokenizer.json | 3 + tokenizer_config.json | 14 ++++ 8 files changed, 357 insertions(+) create mode 100644 .gitattributes create mode 100644 README.md create mode 100644 chat_template.jinja create mode 100644 config.json create mode 100644 generation_config.json create mode 100644 model.safetensors create mode 100644 tokenizer.json create mode 100644 tokenizer_config.json diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..52373fe --- /dev/null +++ b/.gitattributes @@ -0,0 +1,36 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz 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 diff --git a/README.md b/README.md new file mode 100644 index 0000000..40cdf0a --- /dev/null +++ b/README.md @@ -0,0 +1,156 @@ +--- +license: llama3.2 +base_model: meta-llama/Llama-3.2-1B-Instruct +datasets: + - Anthropic/hh-rlhf +language: + - en +library_name: transformers +pipeline_tag: text-generation +tags: + - dpo + - preference-optimization + - rlhf + - llama-3.2 + - from-scratch +--- + +# Llama-3.2-1B-Instruct-DPO-HH + +`meta-llama/Llama-3.2-1B-Instruct` aligned with **Direct Preference Optimization** on Anthropic HH-RLHF, +using a from-scratch DPO implementation — no TRL, no `DPOTrainer`, no Axolotl, no +Lightning. The loss, sequence scoring, completion masking, reference handling and +training loop are all explicit PyTorch. + +This is research and learning code. Read the limitations before using it. + +## Training + +| | | +|---|---| +| Base model | `meta-llama/Llama-3.2-1B-Instruct` | +| Objective | DPO (Rafailov et al., 2023), summed completion log-probabilities | +| Reference | Frozen copy of the base model, live (not cached) | +| Dataset | `Anthropic/hh-rlhf`, `train` split | +| Pairs after parsing | 159,384 | +| Pairs seen | **79,360** (50% of one epoch, 1,240 steps) | +| Hardware | 1× NVIDIA H200 | + +### Hyperparameters + +| | | +|---|---| +| β | 0.1 | +| Learning rate | 5e-07 | +| Schedule | cosine to 0, 10% warmup | +| Optimizer | AdamW, β₁ 0.9, β₂ 0.95, wd 0.0 | +| Effective batch | 64 pairs (8 per device × 8 accumulation) | +| Max grad norm | 1.0 | +| Parameter dtype | **float32** (BF16 autocast for compute) | +| Max length | 1024 (prompt 640, completion 384) | +| Seed | 42 | + +**On precision.** Parameters are stored in FP32 and BF16 is used only for +forward/backward compute. Storing *trainable* parameters in BF16 silently breaks +preference tuning: at a weight of 0.01 the gap between representable BF16 values is +6.1e-5, while an AdamW update at these learning rates is ~1e-6, so updates round to a +no-op while the loss curve still looks plausible. This pipeline rejects BF16 parameter +storage for training outright. + +## Results + +Training metrics only — **no held-out evaluation was run.** These are in-training +statistics on the optimized data, not a measure of generalization. + +| steps | loss | reward margin | reward accuracy | +|---|---|---|---| +| 0–248 | 0.6809 | +0.0420 | 0.548 | +| 248–496 | 0.6513 | +0.1620 | 0.596 | +| 496–744 | 0.6453 | +0.1894 | 0.631 | +| 744–992 | 0.6437 | +0.2152 | 0.618 | +| 992–1240 | 0.6435 | +0.2027 | 0.627 | +| **final** | **0.6455** | **+0.2002** | **0.615** | + +DPO's loss is exactly `log 2 = 0.693147` when policy and reference are identical. This +run began at `0.690420` with implicit rewards of `-0.0112` / +`-0.0176`, confirming the frozen reference and the completion masking were +correct at step 0. + +**Where the margin comes from.** `chosen_reward` moved +0.1242 → +0.0333 +and `rejected_reward` +0.0595 → -0.1797. The separation is +produced mainly by pushing the *rejected* responses below the reference, not by making the +chosen ones more likely — the likelihood-displacement behaviour DPO is known for. Read the +margin as "less likely to produce the dispreferred response", which is not the same claim +as "more likely to produce the preferred one". + +## How large is the effect, really? + +Stated plainly, because a reward margin alone does not tell you this: + +- Relative weight change from the base model: **0.000354** +- Greedy generations that are byte-identical to the base model: **0 of 4** spot-check prompts + +This is a *modest* perturbation of the base model, which is what half an epoch at lr 5e-7 with β=0.1 should produce — β exists precisely to keep the policy near its reference. On general prompts the outputs are recognisably the base model's, with differences in phrasing and formatting. Do not expect a dramatically different assistant. + + +## Usage + +```python +from transformers import AutoModelForCausalLM, AutoTokenizer +import torch + +model_id = "jackf857/Llama-3.2-1B-Instruct-DPO-HH" +tokenizer = AutoTokenizer.from_pretrained(model_id) +model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16, device_map="auto") + +messages = [{"role": "user", "content": "Explain why the sky is blue, briefly."}] +ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt") +out = model.generate(ids.to(model.device), max_new_tokens=128) +print(tokenizer.decode(out[0][ids.shape[-1]:], skip_special_tokens=True)) +``` + +The Llama 3 chat template injects the current date unless `date_string` is pinned. +Training used `date_string="26 Jul 2024"`. + +## Data processing + +HH transcripts were parsed into canonical `(messages, chosen, rejected)` triples with no +chat markup, then rendered through the official Llama 3 chat template. Only the final +assistant response is scored; the completion mask is exactly +`[0]*prompt_len + [1]*completion_len`, enforced by the prompt-prefix invariant. + +Verified against the real Llama 3 tokenizer: exactly one BOS per sequence, 0% BPE merges +across the prompt/completion boundary, every completion ending in `<|eot_id|>`. + + + +## Limitations + +- **No held-out evaluation.** Every number above is a training metric. There is no + evidence here that this model is better than its base — only that DPO optimized what it + was asked to optimize. +- **50% of one epoch on a 1B model.** A short run on a small model. +- **HH-RLHF is noisy.** Preference labels are known to be inconsistent, and many pairs + have no clear quality difference. Some labels prefer epistemic humility ("I don't know") + over confident answers, so the model may become more hedging. +- **Safety is not established.** No safety evaluation was performed. Do not deploy where + harmful output matters. Inherits all limitations of the base model. +- English only; 1B models hallucinate readily. + +## License + +Governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE), +inherited from the base model. That license requires derivative model names to begin with +"Llama". Anthropic HH-RLHF is MIT licensed. + +## Citation + +```bibtex +@inproceedings{rafailov2023direct, + title = {Direct Preference Optimization: Your Language Model is Secretly a Reward Model}, + author = {Rafailov, Rafael and Sharma, Archit and Mitchell, Eric and + Ermon, Stefano and Manning, Christopher D. and Finn, Chelsea}, + booktitle = {Advances in Neural Information Processing Systems}, + year = {2023} +} +``` diff --git a/chat_template.jinja b/chat_template.jinja new file mode 100644 index 0000000..1bad6a0 --- /dev/null +++ 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 %} diff --git a/config.json b/config.json new file mode 100644 index 0000000..e5b077b --- /dev/null +++ b/config.json @@ -0,0 +1,40 @@ +{ + "architectures": [ + "LlamaForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 128000, + "dtype": "float32", + "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": "5.14.1", + "use_cache": false, + "vocab_size": 128256 +} diff --git a/generation_config.json b/generation_config.json new file mode 100644 index 0000000..d7a3756 --- /dev/null +++ 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": "5.14.1" +} diff --git a/model.safetensors b/model.safetensors new file mode 100644 index 0000000..3d094da --- /dev/null +++ b/model.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a6a7864675a5083cd8256abb667850286d9ab823bd0f9d8dc059c6b4317b6695 +size 4943274328 diff --git a/tokenizer.json b/tokenizer.json new file mode 100644 index 0000000..1c1d8d5 --- /dev/null +++ b/tokenizer.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6b9e4e7fb171f92fd137b777cc2714bf87d11576700a1dcd7a399e7bbe39537b +size 17209920 diff --git a/tokenizer_config.json b/tokenizer_config.json new file mode 100644 index 0000000..aadc141 --- /dev/null +++ b/tokenizer_config.json @@ -0,0 +1,14 @@ +{ + "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, + "tokenizer_class": "TokenizersBackend" +}