Instructions to use srs6901/Vikras-MixP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use srs6901/Vikras-MixP with Transformers:

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

pipe = pipeline("text-generation", model="srs6901/Vikras-MixP")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("srs6901/Vikras-MixP", dtype="auto")

llama-cpp-python

How to use srs6901/Vikras-MixP with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="srs6901/Vikras-MixP",
	filename="Vikra-HCT-YeAM-3_3.2_QweLLa-1.7B_Q8_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use srs6901/Vikras-MixP with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf srs6901/Vikras-MixP:Q8_0
# Run inference directly in the terminal:
llama cli -hf srs6901/Vikras-MixP:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf srs6901/Vikras-MixP:Q8_0
# Run inference directly in the terminal:
llama cli -hf srs6901/Vikras-MixP:Q8_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf srs6901/Vikras-MixP:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf srs6901/Vikras-MixP:Q8_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf srs6901/Vikras-MixP:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf srs6901/Vikras-MixP:Q8_0

Use Docker

docker model run hf.co/srs6901/Vikras-MixP:Q8_0

LM Studio
Jan

vLLM

How to use srs6901/Vikras-MixP with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "srs6901/Vikras-MixP"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "srs6901/Vikras-MixP",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/srs6901/Vikras-MixP:Q8_0

SGLang

How to use srs6901/Vikras-MixP 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 "srs6901/Vikras-MixP" \
    --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": "srs6901/Vikras-MixP",
		"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 "srs6901/Vikras-MixP" \
        --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": "srs6901/Vikras-MixP",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use srs6901/Vikras-MixP with Ollama:
```
ollama run hf.co/srs6901/Vikras-MixP:Q8_0
```

Unsloth Studio

How to use srs6901/Vikras-MixP with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for srs6901/Vikras-MixP to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for srs6901/Vikras-MixP to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for srs6901/Vikras-MixP to start chatting

How to use srs6901/Vikras-MixP with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf srs6901/Vikras-MixP:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "srs6901/Vikras-MixP:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use srs6901/Vikras-MixP with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf srs6901/Vikras-MixP:Q8_0

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default srs6901/Vikras-MixP:Q8_0

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use srs6901/Vikras-MixP with Docker Model Runner:
```
docker model run hf.co/srs6901/Vikras-MixP:Q8_0
```

Lemonade

How to use srs6901/Vikras-MixP with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull srs6901/Vikras-MixP:Q8_0

Run and chat with the model

lemonade run user.Vikras-MixP-Q8_0

List all available models

lemonade list

Vikras-MixP / README.md

srs6901

Upload README.md

9f618d1 verified 4 months ago

1.83 kB

library_name: transformers
tags:
  - quantized
  - hybrid
language:
  - ru
  - en

Vikra MixedPrc

12.25B parameter Mistral-based language model with mixed-precision hybrid quantization.

Model Details

Property	Value
Architecture	Mistral (12.25B params, 40 layers)
Hidden size	5120
Attention heads	32 (8 KV heads, GQA)
Intermediate size	14336
Context length	1,024,000 tokens
Vocabulary	131,072 tokens (Tekken BPE)
RoPE theta	1,000,000.0

Quantization: MixP_4.9b_S

Custom mixed-precision quantization scheme with per-tensor type assignment.

Tensor group	Quant type	BPW
`token_embd`, `output`	BF16	16.00
`attn_norm`, `ffn_norm`, `output_norm`	F32	32.00
`attn_q`	Q4_K	4.50
`attn_k`	Q5_K	5.50
`attn_v`	Q3_K	3.44
`attn_output`	Q4_K	4.50
`ffn_gate`	Q3_K	3.44
`ffn_up`	Q5_K	5.50
`ffn_down`	Q5_K / Q6_K (last layers)	5.50–6.56

Overall: 6.11 BPW | Quantized layers only: 4.89 BPW | File size: 8.71 GB

Perplexity

Measured on wikitext-2-raw-test (full dataset, 73 chunks, context 4096 tokens, 299,008 tokens evaluated):

Model	Precision	Size	PPL
Vikra MixP_4.9b_S	6.11 BPW	8.71 GB	5.5000 ± 0.032
Vikhr-Nemo-12B-Instruct (baseline)	BF16	22.81 GB	6.0212 ± 0.034

Chat Template

Built-in chat template (baked into GGUF):

<|start_header_id|>system<|end_header_id|>
{system_message}</s><|start_header_id|>user<|end_header_id|>
{user_message}</s><|start_header_id|>assistant<|end_header_id|>

Usage

# llama.cpp server
llama-server -m Vikra-MixP_4.9b_S.gguf -ngl 25 -c 4096

# llama.cpp CLI
llama-cli -m Vikra-MixP_4.9b_S.gguf -ngl 25 -c 4096 -cnv