Instructions to use AngelRaychev/0.5B-value-iteration_6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AngelRaychev/0.5B-value-iteration_6 with Transformers:

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

pipe = pipeline("text-generation", model="AngelRaychev/0.5B-value-iteration_6")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AngelRaychev/0.5B-value-iteration_6")
model = AutoModelForCausalLM.from_pretrained("AngelRaychev/0.5B-value-iteration_6")
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

vLLM

How to use AngelRaychev/0.5B-value-iteration_6 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AngelRaychev/0.5B-value-iteration_6"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AngelRaychev/0.5B-value-iteration_6",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AngelRaychev/0.5B-value-iteration_6

SGLang

How to use AngelRaychev/0.5B-value-iteration_6 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 "AngelRaychev/0.5B-value-iteration_6" \
    --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": "AngelRaychev/0.5B-value-iteration_6",
		"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 "AngelRaychev/0.5B-value-iteration_6" \
        --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": "AngelRaychev/0.5B-value-iteration_6",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AngelRaychev/0.5B-value-iteration_6 with Docker Model Runner:
```
docker model run hf.co/AngelRaychev/0.5B-value-iteration_6
```

AngelRaychev commited on May 14, 2025

Commit

d32af06

verified ·

1 Parent(s): 7dceaca

Upload Qwen2ForCausalLM

Browse files

Files changed (3) hide show

config.json +4 -2
generation_config.json +2 -10
model.safetensors +1 -1

config.json CHANGED Viewed

@@ -4,13 +4,14 @@
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   "hidden_act": "silu",
   "hidden_size": 896,
   "initializer_range": 0.02,
   "intermediate_size": 4864,
   "max_position_embeddings": 32768,
-  "max_window_layers": 21,
   "model_type": "qwen2",
   "num_attention_heads": 14,
   "num_hidden_layers": 24,
@@ -23,6 +24,7 @@
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   "transformers_version": "4.51.2",
   "use_cache": false,
   "use_sliding_window": false,
   "vocab_size": 151936
 }

   ],
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   "bos_token_id": 151643,
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   "hidden_act": "silu",
   "hidden_size": 896,
   "initializer_range": 0.02,
   "intermediate_size": 4864,
   "max_position_embeddings": 32768,
+  "max_window_layers": 24,
+  "model_card": "\nFinal Loss: 0.4302\nBatch Size: 1024\nLearning Rate: 1e-05\nDataset Size: 203131\n",
   "model_type": "qwen2",
   "num_attention_heads": 14,
   "num_hidden_layers": 24,
   "torch_dtype": "bfloat16",
   "transformers_version": "4.51.2",
   "use_cache": false,
+  "use_mrope": false,
   "use_sliding_window": false,
   "vocab_size": 151936
 }

generation_config.json CHANGED Viewed

@@ -1,14 +1,6 @@
 {
   "bos_token_id": 151643,
-  "do_sample": true,
-  "eos_token_id": [
-    151645,
-    151643
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-  "temperature": 0.7,
-  "top_k": 20,
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   "transformers_version": "4.51.2"
 }

 {
   "bos_token_id": 151643,
+  "eos_token_id": 151643,
+  "max_new_tokens": 2048,
   "transformers_version": "4.51.2"
 }

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