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GRPO-llama3.1-reasoning

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nirusanan commited on Feb 21, 2025

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 ---
 base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
-library_name: peft
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
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-## How to Get Started with the Model
-Use the code below to get started with the model.
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-### Framework versions
-- PEFT 0.14.0

 ---
 base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
+pipeline_tag: text-generation
 ---
+# Model Information
+The GRPO-llama3.1-reasoning is a reasoning trained model of the meta-Llama-3.1-8B-Instruct. The GRPOTrainer supports using custom reward functions instead of dense reward models.
+- **Base model:** meta-llama/meta-Llama-3.1-8B-Instruct
+# How to use
+Starting with Unsloth, reducing memory usage by 80%.
+```python
+import torch
+from huggingface_hub import snapshot_download
+from unsloth import FastLanguageModel, PatchFastRL
+PatchFastRL("GRPO", FastLanguageModel)
+```
+```python
+max_seq_length = 512
+lora_rank = 32
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = "meta-llama/meta-Llama-3.1-8B-Instruct",
+    max_seq_length = max_seq_length,
+    load_in_4bit = True,
+    fast_inference = True,
+    max_lora_rank = lora_rank,
+    gpu_memory_utilization = 0.6,
+)
+```
+```python
+SYSTEM_PROMPT = """
+Respond in the following format:
+<reasoning>
+...
+</reasoning>
+<answer>
+...
+</answer>
+"""
+```
+```python
+model_id = "nirusanan/GRPO-llama3.1-reasoning"
+snapshot_download(repo_id=model_id, local_dir="llama-grpo_saved_lora",
+                  local_dir_use_symlinks=False, revision="main")
+```
+```python
+model.load_adapter("/content/llama-grpo_saved_lora")
+```
+```python
+text = tokenizer.apply_chat_template([
+    {"role" : "system", "content" : SYSTEM_PROMPT},
+    {"role" : "user", "content" : "Calculate pi."},
+], tokenize = False, add_generation_prompt = True)
+from vllm import SamplingParams
+sampling_params = SamplingParams(
+    temperature = 0.8,
+    top_p = 0.95,
+    max_tokens = 1024,
+)
+output = model.fast_generate(
+    text,
+    sampling_params = sampling_params,
+)[0].outputs[0].text
+output
+```