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

srs6901 commited on Feb 14

Commit

9f618d1

verified ·

1 Parent(s): 94e7031

Upload README.md

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README.md +71 -3

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@@ -1,3 +1,71 @@
----
-license: mit
----

+---
+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
+```bash
+# 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
+```