Instructions to use salakash/SamKash-Tolstoy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use salakash/SamKash-Tolstoy with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B")
model = PeftModel.from_pretrained(base_model, "salakash/SamKash-Tolstoy")

Transformers

How to use salakash/SamKash-Tolstoy with Transformers:

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

pipe = pipeline("text-generation", model="salakash/SamKash-Tolstoy")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("salakash/SamKash-Tolstoy", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use salakash/SamKash-Tolstoy with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/salakash/SamKash-Tolstoy

SGLang

How to use salakash/SamKash-Tolstoy 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 "salakash/SamKash-Tolstoy" \
    --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": "salakash/SamKash-Tolstoy",
		"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 "salakash/SamKash-Tolstoy" \
        --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": "salakash/SamKash-Tolstoy",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use salakash/SamKash-Tolstoy with Docker Model Runner:
```
docker model run hf.co/salakash/SamKash-Tolstoy
```

salakash commited on Nov 21, 2025

Commit

22dfea0

verified ·

1 Parent(s): 2c465d6

Update README.md

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -43,7 +43,7 @@ model-index:
 # SamKash-Tolstoy — DeepSeek LoRA (Russian Literature)
-**Developed by Kashif Salahuddin and Samiya Kashif**, **SamKash-Tolstoy** is a domain-specialized LLM (lightweight LoRA adapter) built exclusively for Russian literature. It’s trained on **475 public-domain Russian classics** from the Project Gutenberg collection and enriched with **university and critics’ articles** filtered from the **OSCAR** web corpus, so the voice and psychological depth feel authentic without using any copyrighted books.
 **Reasoning-forward core:** Based on `deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B`, giving strong structure and long-form coherence; further supervised fine-tuning from output feedback reduces drift and hallucinations over time.

 # SamKash-Tolstoy — DeepSeek LoRA (Russian Literature)
+**Developed by Samiya Kashif and Kashif Salahuddin**, **SamKash-Tolstoy** is a domain-specialized LLM (lightweight LoRA adapter) built exclusively for Russian literature. It’s trained on **475 public-domain Russian classics** from the Project Gutenberg collection and enriched with **university and critics’ articles** filtered from the **OSCAR** web corpus, so the voice and psychological depth feel authentic without using any copyrighted books.
 **Reasoning-forward core:** Based on `deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B`, giving strong structure and long-form coherence; further supervised fine-tuning from output feedback reduces drift and hallucinations over time.