Instructions to use MathGenie/Mistral-7B-Ours-SFT-SCDPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MathGenie/Mistral-7B-Ours-SFT-SCDPO with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="MathGenie/Mistral-7B-Ours-SFT-SCDPO")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MathGenie/Mistral-7B-Ours-SFT-SCDPO")
model = AutoModelForCausalLM.from_pretrained("MathGenie/Mistral-7B-Ours-SFT-SCDPO", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MathGenie/Mistral-7B-Ours-SFT-SCDPO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MathGenie/Mistral-7B-Ours-SFT-SCDPO"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MathGenie/Mistral-7B-Ours-SFT-SCDPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MathGenie/Mistral-7B-Ours-SFT-SCDPO

SGLang

How to use MathGenie/Mistral-7B-Ours-SFT-SCDPO with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "MathGenie/Mistral-7B-Ours-SFT-SCDPO" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MathGenie/Mistral-7B-Ours-SFT-SCDPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "MathGenie/Mistral-7B-Ours-SFT-SCDPO" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MathGenie/Mistral-7B-Ours-SFT-SCDPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MathGenie/Mistral-7B-Ours-SFT-SCDPO with Docker Model Runner:
```
docker model run hf.co/MathGenie/Mistral-7B-Ours-SFT-SCDPO
```

luzimu commited on Jul 4, 2024

Commit

a1a8d9b

1 Parent(s): 5e1c8ef

first commit

Browse files

Files changed (19) hide show

README.md +81 -3
added_tokens.json +10 -0
all_results.json +21 -0
config.json +26 -0
eval_results.json +16 -0
generation_config.json +6 -0
model-00001-of-00003.safetensors +3 -0
model-00002-of-00003.safetensors +3 -0
model-00003-of-00003.safetensors +3 -0
model.safetensors.index.json +298 -0
runs/Apr13_21-08-49_pt-4rcsf1vl-worker-0/events.out.tfevents.1713013930.pt-4rcsf1vl-worker-0.38.0 +3 -0
runs/Apr13_21-08-49_pt-4rcsf1vl-worker-0/events.out.tfevents.1713028337.pt-4rcsf1vl-worker-0.38.1 +3 -0
special_tokens_map.json +40 -0
tokenizer.json +0 -0
tokenizer.model +3 -0
tokenizer_config.json +116 -0
train_results.json +8 -0
trainer_state.json +1584 -0
training_args.bin +3 -0

README.md CHANGED Viewed

@@ -1,3 +1,81 @@
----
-license: apache-2.0
----

+---
+base_model: /mnt/cache/luzimu/rlhf_math/alignment-handbook/outs/Mistral-7B-v0.1-lce
+tags:
+- alignment-handbook
+- generated_from_trainer
+datasets:
+- /mnt/cache/luzimu/rlhf_math/data/controled_steps_math_gsm8k_lce_dpo_ascend_lim2_lim3_add_dpo1x1
+model-index:
+- name: Mistral-7B-v0.1-lce_controled_steps_dpo_ascend_lim2_lim3_add_dpo1x1
+  results: []
+---
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+# Mistral-7B-v0.1-lce_controled_steps_dpo_ascend_lim2_lim3_add_dpo1x1
+This model is a fine-tuned version of [/mnt/cache/luzimu/rlhf_math/alignment-handbook/outs/Mistral-7B-v0.1-lce](https://huggingface.co//mnt/cache/luzimu/rlhf_math/alignment-handbook/outs/Mistral-7B-v0.1-lce) on the /mnt/cache/luzimu/rlhf_math/data/controled_steps_math_gsm8k_lce_dpo_ascend_lim2_lim3_add_dpo1x1 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1793
+- Rewards/chosen: 0.2587
+- Rewards/rejected: -7.0301
+- Rewards/accuracies: 0.8947
+- Rewards/margins: 7.2889
+- Logps/rejected: -253.7773
+- Logps/chosen: -80.3105
+- Logits/rejected: -2.3417
+- Logits/chosen: -2.3846
+## Model description
+More information needed
+## Intended uses & limitations
+More information needed
+## Training and evaluation data
+More information needed
+## Training procedure
+### Training hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 1e-07
+- train_batch_size: 2
+- eval_batch_size: 4
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 4
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 64
+- total_eval_batch_size: 16
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 2
+### Training results
+| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
+|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
+| 0.3963        | 0.21  | 100  | 0.3636          | 1.8634         | -0.1518          | 0.8816             | 2.0152          | -184.9944      | -64.2644     | -2.7112         | -2.7505       |
+| 0.2849        | 0.43  | 200  | 0.2598          | 0.7706         | -3.7221          | 0.8816             | 4.4927          | -220.6974      | -75.1921     | -2.5067         | -2.5475       |
+| 0.2496        | 0.64  | 300  | 0.2295          | 0.9323         | -4.2717          | 0.8684             | 5.2040          | -226.1934      | -73.5753     | -2.5080         | -2.5494       |
+| 0.2331        | 0.86  | 400  | 0.2089          | 0.7871         | -4.8912          | 0.8684             | 5.6783          | -232.3884      | -75.0269     | -2.4967         | -2.5382       |
+| 0.0874        | 1.07  | 500  | 0.1872          | 0.6345         | -5.7444          | 0.8816             | 6.3789          | -240.9202      | -76.5527     | -2.4323         | -2.4761       |
+| 0.1217        | 1.28  | 600  | 0.1832          | 0.2282         | -6.6907          | 0.8684             | 6.9188          | -250.3827      | -80.6161     | -2.3741         | -2.4172       |
+| 0.0966        | 1.5   | 700  | 0.1807          | 0.1849         | -7.0125          | 0.8816             | 7.1975          | -253.6012      | -81.0485     | -2.3503         | -2.3940       |
+| 0.0755        | 1.71  | 800  | 0.1802          | 0.3224         | -6.9539          | 0.8947             | 7.2763          | -253.0150      | -79.6739     | -2.3437         | -2.3867       |
+| 0.1177        | 1.93  | 900  | 0.1793          | 0.2587         | -7.0301          | 0.8947             | 7.2889          | -253.7773      | -80.3105     | -2.3417         | -2.3846       |
+### Framework versions
+- Transformers 4.38.2
+- Pytorch 2.1.2
+- Datasets 2.14.6
+- Tokenizers 0.15.2

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all_results.json ADDED Viewed

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+{
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+    "eval_logits/rejected": -2.3414511680603027,
+    "eval_logps/chosen": -80.37240600585938,
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+    "eval_rewards/accuracies": 0.8947368264198303,
+    "eval_rewards/chosen": 0.25255322456359863,
+    "eval_rewards/margins": 7.301982879638672,
+    "eval_rewards/rejected": -7.049429416656494,
+    "eval_runtime": 27.9087,
+    "eval_samples": 301,
+    "eval_samples_per_second": 10.785,
+    "eval_steps_per_second": 0.681,
+    "train_loss": 0.21607095603003493,
+    "train_runtime": 14362.1771,
+    "train_samples": 29893,
+    "train_samples_per_second": 4.163,
+    "train_steps_per_second": 0.065
+}

config.json ADDED Viewed

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+{
+  "_name_or_path": "/mnt/cache/luzimu/rlhf_math/alignment-handbook/outs/Mistral-7B-v0.1-lce",
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 32768,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
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+  "use_cache": true,
+  "vocab_size": 32128
+}

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+    "eval_rewards/accuracies": 0.8947368264198303,
+    "eval_rewards/chosen": 0.25255322456359863,
+    "eval_rewards/margins": 7.301982879638672,
+    "eval_rewards/rejected": -7.049429416656494,
+    "eval_runtime": 27.9087,
+    "eval_samples": 301,
+    "eval_samples_per_second": 10.785,
+    "eval_steps_per_second": 0.681
+}

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+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
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