Instructions to use venkateshchsagalm/SagaLM-slm1-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use venkateshchsagalm/SagaLM-slm1-merged with Transformers:

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

pipe = pipeline("text-generation", model="venkateshchsagalm/SagaLM-slm1-merged")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("venkateshchsagalm/SagaLM-slm1-merged")
model = AutoModelForCausalLM.from_pretrained("venkateshchsagalm/SagaLM-slm1-merged", 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 venkateshchsagalm/SagaLM-slm1-merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "venkateshchsagalm/SagaLM-slm1-merged"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "venkateshchsagalm/SagaLM-slm1-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/venkateshchsagalm/SagaLM-slm1-merged

SGLang

How to use venkateshchsagalm/SagaLM-slm1-merged 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 "venkateshchsagalm/SagaLM-slm1-merged" \
    --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": "venkateshchsagalm/SagaLM-slm1-merged",
		"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 "venkateshchsagalm/SagaLM-slm1-merged" \
        --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": "venkateshchsagalm/SagaLM-slm1-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use venkateshchsagalm/SagaLM-slm1-merged with Docker Model Runner:
```
docker model run hf.co/venkateshchsagalm/SagaLM-slm1-merged
```

SagaLM-slm1 (merged, standalone)

Standalone merged model: Qwen/Qwen2.5-3B-Instruct + SagaLM LoRA training.

Training Data (12 sources, long-form filtered)

OpenHermes 2.5, UltraChat 200k, OpenOrca, MetaMathQA,
Function Calling (hypervariance), Open-Platypus,
Glaive Code Assistant, Dolly 15k, ShareGPT GPT-4,
Alpaca-GPT4, SlimOrca, MathInstruct

Only samples with detailed, long assistant answers were kept, training the model to always respond comprehensively.

Identity Reinforcement

5 identity-focused training samples were injected to ensure the model consistently identifies as SagaLM (not Qwen or any other model).

How to use

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("venkateshchsagalm/SagaLM-slm1-merged", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("venkateshchsagalm/SagaLM-slm1-merged")

For best long answers, generate with:

model.generate(**inputs, max_new_tokens=2048, min_new_tokens=250,
               temperature=0.7, top_p=0.9, repetition_penalty=1.1,
               do_sample=True)

Downloads last month: 14

Safetensors

Model size

3B params

Tensor type

F16

Model tree for venkateshchsagalm/SagaLM-slm1-merged

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-3B-Instruct

Finetuned

(1563)

this model