Instructions to use QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF", dtype="auto") - llama-cpp-python
How to use QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF", filename="sepctrum-ties-sqlcoder-8b.Q2_K.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF:Q4_K_M
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 QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF:Q4_K_M
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 QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF:Q4_K_M
Use Docker
docker model run hf.co/QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF with Ollama:
ollama run hf.co/QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF:Q4_K_M
- Unsloth Studio new
How to use QuantFactory/sepctrum-ties-sqlcoder-8b-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 QuantFactory/sepctrum-ties-sqlcoder-8b-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 QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF to start chatting
- Docker Model Runner
How to use QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF with Docker Model Runner:
docker model run hf.co/QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF:Q4_K_M
- Lemonade
How to use QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.sepctrum-ties-sqlcoder-8b-GGUF-Q4_K_M
List all available models
lemonade list
Upload README.md with huggingface_hub
Browse files
README.md
ADDED
|
@@ -0,0 +1,86 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
|
| 2 |
+
---
|
| 3 |
+
|
| 4 |
+
base_model:
|
| 5 |
+
- defog/llama-3-sqlcoder-8b
|
| 6 |
+
- meta-llama/Meta-Llama-3-8B-Instruct
|
| 7 |
+
library_name: transformers
|
| 8 |
+
tags:
|
| 9 |
+
- mergekit
|
| 10 |
+
- merge
|
| 11 |
+
|
| 12 |
+
|
| 13 |
+
---
|
| 14 |
+
|
| 15 |
+

|
| 16 |
+
|
| 17 |
+
# QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF
|
| 18 |
+
This is quantized version of [arcee-ai/sepctrum-ties-sqlcoder-8b](https://huggingface.co/arcee-ai/sepctrum-ties-sqlcoder-8b) created using llama.cpp
|
| 19 |
+
|
| 20 |
+
# Original Model Card
|
| 21 |
+
|
| 22 |
+
# merge
|
| 23 |
+
|
| 24 |
+
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
|
| 25 |
+
|
| 26 |
+
## Merge Details
|
| 27 |
+
### Merge Method
|
| 28 |
+
|
| 29 |
+
This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) as a base.
|
| 30 |
+
|
| 31 |
+
### Models Merged
|
| 32 |
+
|
| 33 |
+
The following models were included in the merge:
|
| 34 |
+
* [defog/llama-3-sqlcoder-8b](https://huggingface.co/defog/llama-3-sqlcoder-8b)
|
| 35 |
+
|
| 36 |
+
### Configuration
|
| 37 |
+
|
| 38 |
+
The following YAML configuration was used to produce this model:
|
| 39 |
+
|
| 40 |
+
```yaml
|
| 41 |
+
merge_method: ties
|
| 42 |
+
base_model: meta-llama/Meta-Llama-3-8B-Instruct
|
| 43 |
+
models:
|
| 44 |
+
- model: defog/llama-3-sqlcoder-8b
|
| 45 |
+
parameters:
|
| 46 |
+
weight:
|
| 47 |
+
- filter: mlp.down_proj
|
| 48 |
+
value: [0, 0, 0, 0, 0, 0, 0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0, 0.5, 0, 0, 0, 0, 0, 0.5, 0.5, 0.5, 0.5, 0.5, 0, 0, 0]
|
| 49 |
+
- filter: mlp.gate_proj
|
| 50 |
+
value: [0, 0, 0, 0, 0, 0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0.5]
|
| 51 |
+
- filter: mlp.up_proj
|
| 52 |
+
value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0.5, 0, 0, 0, 0, 0.5, 0, 0.5, 0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
|
| 53 |
+
- filter: self_attn.k_proj
|
| 54 |
+
value: [0.5, 0.5, 0.5, 0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0, 0.5, 0.5, 0.5, 0.5, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0]
|
| 55 |
+
- filter: self_attn.o_proj
|
| 56 |
+
value: [0.5, 0.5, 0.5, 0.5, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0.5, 0.5, 0.5, 0.5, 0, 0.5, 0, 0, 0, 0.5, 0.5, 0.5, 0.5, 0.5, 0]
|
| 57 |
+
- filter: self_attn.q_proj
|
| 58 |
+
value: [0, 0, 0.5, 0.5, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0, 0.5, 0.5, 0.5, 0.5, 0.5]
|
| 59 |
+
- filter: self_attn.v_proj
|
| 60 |
+
value: [0.5, 0, 0.5, 0, 0, 0.5, 0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0, 0, 0, 0, 0.5, 0.5, 0, 0, 0, 0, 0.5, 0, 0, 0.5, 0, 0, 0.5, 0.5]
|
| 61 |
+
- value: [0]
|
| 62 |
+
density: 0.75
|
| 63 |
+
- model: meta-llama/Meta-Llama-3-8B-Instruct
|
| 64 |
+
parameters:
|
| 65 |
+
weight:
|
| 66 |
+
- filter: mlp.down_proj
|
| 67 |
+
value: [1, 1, 1, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 0.5, 1, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1, 1]
|
| 68 |
+
- filter: mlp.gate_proj
|
| 69 |
+
value: [1, 1, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 0.5]
|
| 70 |
+
- filter: mlp.up_proj
|
| 71 |
+
value: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 0.5, 1, 1, 1, 1, 0.5, 1, 0.5, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
|
| 72 |
+
- filter: self_attn.k_proj
|
| 73 |
+
value: [0.5, 0.5, 0.5, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 1]
|
| 74 |
+
- filter: self_attn.o_proj
|
| 75 |
+
value: [0.5, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 0.5, 1, 1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 1]
|
| 76 |
+
- filter: self_attn.q_proj
|
| 77 |
+
value: [1, 1, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 0.5, 0.5, 0.5, 0.5, 0.5]
|
| 78 |
+
- filter: self_attn.v_proj
|
| 79 |
+
value: [0.5, 1, 0.5, 1, 1, 0.5, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 0.5, 0.5, 1, 1, 1, 1, 0.5, 1, 1, 0.5, 1, 1, 0.5, 0.5]
|
| 80 |
+
- value: [1]
|
| 81 |
+
density: 1.0
|
| 82 |
+
parameters: {normalize: true, int8_mask: true}
|
| 83 |
+
dtype: bfloat16
|
| 84 |
+
|
| 85 |
+
```
|
| 86 |
+
|