Instructions to use tensorblock/semcoder_1030-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tensorblock/semcoder_1030-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="tensorblock/semcoder_1030-GGUF") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("tensorblock/semcoder_1030-GGUF", dtype="auto") - llama-cpp-python
How to use tensorblock/semcoder_1030-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="tensorblock/semcoder_1030-GGUF", filename="semcoder_1030-Q2_K.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use tensorblock/semcoder_1030-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf tensorblock/semcoder_1030-GGUF:Q2_K # Run inference directly in the terminal: llama-cli -hf tensorblock/semcoder_1030-GGUF:Q2_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf tensorblock/semcoder_1030-GGUF:Q2_K # Run inference directly in the terminal: llama-cli -hf tensorblock/semcoder_1030-GGUF:Q2_K
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 tensorblock/semcoder_1030-GGUF:Q2_K # Run inference directly in the terminal: ./llama-cli -hf tensorblock/semcoder_1030-GGUF:Q2_K
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 tensorblock/semcoder_1030-GGUF:Q2_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf tensorblock/semcoder_1030-GGUF:Q2_K
Use Docker
docker model run hf.co/tensorblock/semcoder_1030-GGUF:Q2_K
- LM Studio
- Jan
- vLLM
How to use tensorblock/semcoder_1030-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tensorblock/semcoder_1030-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tensorblock/semcoder_1030-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/tensorblock/semcoder_1030-GGUF:Q2_K
- SGLang
How to use tensorblock/semcoder_1030-GGUF 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 "tensorblock/semcoder_1030-GGUF" \ --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": "tensorblock/semcoder_1030-GGUF", "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 "tensorblock/semcoder_1030-GGUF" \ --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": "tensorblock/semcoder_1030-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use tensorblock/semcoder_1030-GGUF with Ollama:
ollama run hf.co/tensorblock/semcoder_1030-GGUF:Q2_K
- Unsloth Studio new
How to use tensorblock/semcoder_1030-GGUF 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 tensorblock/semcoder_1030-GGUF 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 tensorblock/semcoder_1030-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for tensorblock/semcoder_1030-GGUF to start chatting
- Docker Model Runner
How to use tensorblock/semcoder_1030-GGUF with Docker Model Runner:
docker model run hf.co/tensorblock/semcoder_1030-GGUF:Q2_K
- Lemonade
How to use tensorblock/semcoder_1030-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tensorblock/semcoder_1030-GGUF:Q2_K
Run and chat with the model
lemonade run user.semcoder_1030-GGUF-Q2_K
List all available models
lemonade list
Upload folder using huggingface_hub
Browse files- .gitattributes +12 -0
- README.md +85 -0
- semcoder_1030-Q2_K.gguf +3 -0
- semcoder_1030-Q3_K_L.gguf +3 -0
- semcoder_1030-Q3_K_M.gguf +3 -0
- semcoder_1030-Q3_K_S.gguf +3 -0
- semcoder_1030-Q4_0.gguf +3 -0
- semcoder_1030-Q4_K_M.gguf +3 -0
- semcoder_1030-Q4_K_S.gguf +3 -0
- semcoder_1030-Q5_0.gguf +3 -0
- semcoder_1030-Q5_K_M.gguf +3 -0
- semcoder_1030-Q5_K_S.gguf +3 -0
- semcoder_1030-Q6_K.gguf +3 -0
- semcoder_1030-Q8_0.gguf +3 -0
.gitattributes
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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semcoder_1030-Q2_K.gguf filter=lfs diff=lfs merge=lfs -text
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semcoder_1030-Q3_K_L.gguf filter=lfs diff=lfs merge=lfs -text
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semcoder_1030-Q3_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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semcoder_1030-Q3_K_S.gguf filter=lfs diff=lfs merge=lfs -text
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semcoder_1030-Q4_0.gguf filter=lfs diff=lfs merge=lfs -text
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semcoder_1030-Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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semcoder_1030-Q6_K.gguf filter=lfs diff=lfs merge=lfs -text
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semcoder_1030-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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| 2 |
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license: other
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library_name: transformers
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| 4 |
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license_name: deepseek
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+
license_link: https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/LICENSE-MODEL
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pipeline_tag: text-generation
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tags:
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+
- TensorBlock
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| 9 |
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- GGUF
|
| 10 |
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base_model: semcoder/semcoder_1030
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| 11 |
+
---
|
| 12 |
+
|
| 13 |
+
<div style="width: auto; margin-left: auto; margin-right: auto">
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| 14 |
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<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
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</div>
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| 16 |
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<div style="display: flex; justify-content: space-between; width: 100%;">
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<div style="display: flex; flex-direction: column; align-items: flex-start;">
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| 18 |
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<p style="margin-top: 0.5em; margin-bottom: 0em;">
|
| 19 |
+
Feedback and support: TensorBlock's <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a>
|
| 20 |
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</p>
|
| 21 |
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</div>
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| 22 |
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</div>
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| 23 |
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| 24 |
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## semcoder/semcoder_1030 - GGUF
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| 25 |
+
|
| 26 |
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This repo contains GGUF format model files for [semcoder/semcoder_1030](https://huggingface.co/semcoder/semcoder_1030).
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|
| 28 |
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The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4242](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).
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| 29 |
+
|
| 30 |
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<div style="text-align: left; margin: 20px 0;">
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| 31 |
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<a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
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| 32 |
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Run them on the TensorBlock client using your local machine ↗
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</a>
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</div>
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| 36 |
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## Prompt template
|
| 37 |
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| 38 |
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```
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| 39 |
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<|begin▁of▁sentence|>You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable <Code> according to <NL_Description>
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| 40 |
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|
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<NL_Description>
|
| 42 |
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{prompt}
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| 43 |
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<Code>
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| 45 |
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```
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| 46 |
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| 47 |
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## Model file specification
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| 48 |
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| 49 |
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| Filename | Quant type | File Size | Description |
|
| 50 |
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| -------- | ---------- | --------- | ----------- |
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| 51 |
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| [semcoder_1030-Q2_K.gguf](https://huggingface.co/tensorblock/semcoder_1030-GGUF/blob/main/semcoder_1030-Q2_K.gguf) | Q2_K | 2.535 GB | smallest, significant quality loss - not recommended for most purposes |
|
| 52 |
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| [semcoder_1030-Q3_K_S.gguf](https://huggingface.co/tensorblock/semcoder_1030-GGUF/blob/main/semcoder_1030-Q3_K_S.gguf) | Q3_K_S | 2.950 GB | very small, high quality loss |
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| 53 |
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| [semcoder_1030-Q3_K_M.gguf](https://huggingface.co/tensorblock/semcoder_1030-GGUF/blob/main/semcoder_1030-Q3_K_M.gguf) | Q3_K_M | 3.300 GB | very small, high quality loss |
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| 54 |
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| [semcoder_1030-Q3_K_L.gguf](https://huggingface.co/tensorblock/semcoder_1030-GGUF/blob/main/semcoder_1030-Q3_K_L.gguf) | Q3_K_L | 3.599 GB | small, substantial quality loss |
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| 55 |
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| [semcoder_1030-Q4_0.gguf](https://huggingface.co/tensorblock/semcoder_1030-GGUF/blob/main/semcoder_1030-Q4_0.gguf) | Q4_0 | 3.828 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
|
| 56 |
+
| [semcoder_1030-Q4_K_S.gguf](https://huggingface.co/tensorblock/semcoder_1030-GGUF/blob/main/semcoder_1030-Q4_K_S.gguf) | Q4_K_S | 3.859 GB | small, greater quality loss |
|
| 57 |
+
| [semcoder_1030-Q4_K_M.gguf](https://huggingface.co/tensorblock/semcoder_1030-GGUF/blob/main/semcoder_1030-Q4_K_M.gguf) | Q4_K_M | 4.083 GB | medium, balanced quality - recommended |
|
| 58 |
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| [semcoder_1030-Q5_0.gguf](https://huggingface.co/tensorblock/semcoder_1030-GGUF/blob/main/semcoder_1030-Q5_0.gguf) | Q5_0 | 4.654 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
|
| 59 |
+
| [semcoder_1030-Q5_K_S.gguf](https://huggingface.co/tensorblock/semcoder_1030-GGUF/blob/main/semcoder_1030-Q5_K_S.gguf) | Q5_K_S | 4.654 GB | large, low quality loss - recommended |
|
| 60 |
+
| [semcoder_1030-Q5_K_M.gguf](https://huggingface.co/tensorblock/semcoder_1030-GGUF/blob/main/semcoder_1030-Q5_K_M.gguf) | Q5_K_M | 4.785 GB | large, very low quality loss - recommended |
|
| 61 |
+
| [semcoder_1030-Q6_K.gguf](https://huggingface.co/tensorblock/semcoder_1030-GGUF/blob/main/semcoder_1030-Q6_K.gguf) | Q6_K | 5.531 GB | very large, extremely low quality loss |
|
| 62 |
+
| [semcoder_1030-Q8_0.gguf](https://huggingface.co/tensorblock/semcoder_1030-GGUF/blob/main/semcoder_1030-Q8_0.gguf) | Q8_0 | 7.164 GB | very large, extremely low quality loss - not recommended |
|
| 63 |
+
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| 64 |
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| 65 |
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## Downloading instruction
|
| 66 |
+
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| 67 |
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### Command line
|
| 68 |
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| 69 |
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Firstly, install Huggingface Client
|
| 70 |
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| 71 |
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```shell
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| 72 |
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pip install -U "huggingface_hub[cli]"
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```
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Then, downoad the individual model file the a local directory
|
| 76 |
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| 77 |
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```shell
|
| 78 |
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huggingface-cli download tensorblock/semcoder_1030-GGUF --include "semcoder_1030-Q2_K.gguf" --local-dir MY_LOCAL_DIR
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| 79 |
+
```
|
| 80 |
+
|
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If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:
|
| 82 |
+
|
| 83 |
+
```shell
|
| 84 |
+
huggingface-cli download tensorblock/semcoder_1030-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
|
| 85 |
+
```
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semcoder_1030-Q2_K.gguf
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oid sha256:fc751d20088a4dd189a38ffe645ded2ff98fa41b95cc6922ed7793dddde5d7f0
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size 2534501408
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oid sha256:f56147b738368d2e07fe52609f81613680a01fbc6746303a75cd51f54434c332
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oid sha256:e4be9445690d880746d855916a991923b705f01c5b00ada55013576db598cb66
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size 3299748896
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version https://git-lfs.github.com/spec/v1
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oid sha256:98729b2ec40af1a2673a223293858b13c8b65a1738a5a8c35433b96827bcdcf9
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size 2950048800
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version https://git-lfs.github.com/spec/v1
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size 3827690528
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version https://git-lfs.github.com/spec/v1
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oid sha256:6305fdf1c4a24ffd90392c0e28f05b7a838093afdc9f7f22d071f4f0ef638ae7
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+
size 4082887712
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semcoder_1030-Q4_K_S.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:b5bcc3da8fa8ea482f1cf4180faee17b84281342ee675a97bba038e1ba522732
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size 3858623520
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semcoder_1030-Q5_0.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:a0ac2dd711fa64578bfe5c70995e48a9cac06c3b8a2985b90a257de08d6ef8db
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size 4653706272
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version https://git-lfs.github.com/spec/v1
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semcoder_1030-Q6_K.gguf
ADDED
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version https://git-lfs.github.com/spec/v1
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ADDED
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version https://git-lfs.github.com/spec/v1
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