Instructions to use suayptalha/DeepSeek-R1-Distill-Llama-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use suayptalha/DeepSeek-R1-Distill-Llama-3B with Transformers:

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

pipe = pipeline("text-generation", model="suayptalha/DeepSeek-R1-Distill-Llama-3B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("suayptalha/DeepSeek-R1-Distill-Llama-3B")
model = AutoModelForCausalLM.from_pretrained("suayptalha/DeepSeek-R1-Distill-Llama-3B")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use suayptalha/DeepSeek-R1-Distill-Llama-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "suayptalha/DeepSeek-R1-Distill-Llama-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "suayptalha/DeepSeek-R1-Distill-Llama-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/suayptalha/DeepSeek-R1-Distill-Llama-3B

SGLang

How to use suayptalha/DeepSeek-R1-Distill-Llama-3B 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 "suayptalha/DeepSeek-R1-Distill-Llama-3B" \
    --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": "suayptalha/DeepSeek-R1-Distill-Llama-3B",
		"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 "suayptalha/DeepSeek-R1-Distill-Llama-3B" \
        --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": "suayptalha/DeepSeek-R1-Distill-Llama-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use suayptalha/DeepSeek-R1-Distill-Llama-3B with Docker Model Runner:
```
docker model run hf.co/suayptalha/DeepSeek-R1-Distill-Llama-3B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

DeepSeek-R1-Distill-Llama-3B

This model is the distilled version of DeepSeek-R1 on Llama-3.2-3B with R1-Distill-SFT dataset.

See axolotl config

base_model: unsloth/Llama-3.2-3B-Instruct
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: true
load_in_4bit: false
strict: false

chat_template: llama3
datasets:
  - path: ./custom_dataset.json
    type: chat_template
    conversation: chatml
    ds_type: json

add_bos_token: true
add_eos_token: true
use_default_system_prompt: false

special_tokens:
  bos_token: "<|begin_of_text|>"
  eos_token: "<|eot_id|>"
  pad_token: "<|eot_id|>"
  additional_special_tokens:
    - "<|begin_of_text|>"
    - "<|eot_id|>"

adapter: lora
lora_model_dir:
lora_r: 16
lora_alpha: 32
lora_dropout: 0.1
lora_target_linear: true

hub_model_id: suayptalha/DeepSeek-R1-Distill-Llama-3B

sequence_len: 2048
sample_packing: false
pad_to_sequence_len: true
micro_batch_size: 2
gradient_accumulation_steps: 8
num_epochs: 1
learning_rate: 2e-5
optimizer: paged_adamw_8bit
lr_scheduler: cosine

train_on_inputs: false
group_by_length: false
bf16: false
fp16: true
tf32: false

gradient_checkpointing: true
flash_attention: false

logging_steps: 50
warmup_steps: 100
saves_per_epoch: 1

output_dir: ./finetune-sft-results
save_safetensors: true

Prompt Template

You can use Llama3 prompt template while using the model:

Llama3

<|start_header_id|>system<|end_header_id|>
{system}<|eot_id|>

<|start_header_id|>user<|end_header_id|>
{user}<|eot_id|>

<|start_header_id|>assistant<|end_header_id|>
{assistant}<|eot_id|>

Example usage:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "suayptalha/DeepSeek-R1-Distill-Llama-3B",
    device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained("suayptalha/DeepSeek-R1-Distill-Llama-3B")

SYSTEM_PROMPT = """Respond in the following format:
<think>
You should reason between these tags.
</think>

Answer goes here...

Always use <think> </think> tags even if they are not necessary.
"""

messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {"role": "user", "content": "Which one is larger? 9.11 or 9.9?"},
]
inputs = tokenizer.apply_chat_template(
    messages,
    tokenize = True,
    add_generation_prompt = True,
    return_tensors = "pt",
).to("cuda")
output = model.generate(input_ids=inputs, max_new_tokens=256, use_cache=True, temperature=0.7)
decoded_output = tokenizer.decode(output[0], skip_special_tokens=False)
print(decoded_output)

Output:

<think>
First, I need to compare the two numbers 9.11 and 9.9. 

Next, I'll analyze each number. The first digit after the decimal point in 9.11 is 1, and in 9.9, it's 9. 

Since 9 is greater than 1, 9.9 is larger than 9.11.
</think>

To determine which number is larger, let's compare the two numbers:

**9.11** and **9.9**

1. **Identify the Decimal Places:**
   - Both numbers have two decimal places.
   
2. **Compare the Tens Place (Right of the Decimal Point):**
   - **9.11:** The tens place is 1.
   - **9.9:** The tens place is 9.
   
3. **Conclusion:**
   - Since 9 is greater than 1, the number with the larger tens place is 9.9.
   
**Answer:** **9.9** is larger than **9.11**.

Suggested system prompt:

Respond in the following format:
<think>
You should reason between these tags.
</think>

Answer goes here...

Always use <think> </think> tags even if they are not necessary.