Instructions to use witflag/Theta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use witflag/Theta with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="witflag/Theta", filename="Qwen3-14B-Gemini-3-Pro-Preview-Distill.bf16.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use witflag/Theta with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf witflag/Theta:Q4_K_M # Run inference directly in the terminal: llama-cli -hf witflag/Theta:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf witflag/Theta:Q4_K_M # Run inference directly in the terminal: llama-cli -hf witflag/Theta: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 witflag/Theta:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf witflag/Theta: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 witflag/Theta:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf witflag/Theta:Q4_K_M
Use Docker
docker model run hf.co/witflag/Theta:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use witflag/Theta with Ollama:
ollama run hf.co/witflag/Theta:Q4_K_M
- Unsloth Studio
How to use witflag/Theta 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 witflag/Theta 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 witflag/Theta to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for witflag/Theta to start chatting
- Pi
How to use witflag/Theta with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf witflag/Theta:Q4_K_M
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": "witflag/Theta:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use witflag/Theta with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf witflag/Theta:Q4_K_M
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 witflag/Theta:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use witflag/Theta with Docker Model Runner:
docker model run hf.co/witflag/Theta:Q4_K_M
- Lemonade
How to use witflag/Theta with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull witflag/Theta:Q4_K_M
Run and chat with the model
lemonade run user.Theta-Q4_K_M
List all available models
lemonade list
Duplicate from TeichAI/Qwen3-14B-Gemini-3-Pro-Preview-High-Reasoning-Distill-GGUF
Browse files- .gitattributes +43 -0
- Modelfile +59 -0
- Qwen3-14B-Gemini-3-Pro-Preview-Distill.bf16.gguf +3 -0
- Qwen3-14B-Gemini-3-Pro-Preview-Distill.iq4_nl.gguf +3 -0
- Qwen3-14B-Gemini-3-Pro-Preview-Distill.q3_k_m.gguf +3 -0
- Qwen3-14B-Gemini-3-Pro-Preview-Distill.q3_k_s.gguf +3 -0
- Qwen3-14B-Gemini-3-Pro-Preview-Distill.q4_k_m.gguf +3 -0
- Qwen3-14B-Gemini-3-Pro-Preview-Distill.q8_0.gguf +3 -0
- README.md +39 -0
- config.json +75 -0
- qwen3-14b.F16.gguf +3 -0
- qwen3-14b.Q8_0.gguf +3 -0
.gitattributes
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qwen3-14b.Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
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Qwen3-14B-Gemini-3-Pro-Preview-Distill.q3_k_s.gguf filter=lfs diff=lfs merge=lfs -text
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FROM qwen3-14b.Q8_0.gguf
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TEMPLATE """{{- if .Messages }}
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{{- if or .System .Tools }}<|im_start|>system
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{{- if .System }}
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{{ .System }}
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{{- end }}
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{{- if .Tools }}
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# Tools
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You may call one or more functions to assist with the user query.
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You are provided with function signatures within <tools></tools> XML tags:
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<tools>
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{{- range .Tools }}
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{"type": "function", "function": {{ .Function }}}
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{{- end }}
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</tools>
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For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
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<tool_call>
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{"name": <function-name>, "arguments": <args-json-object>}
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</tool_call>
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{{- end }}<|im_end|>
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{{ end }}
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{{- range $i, $_ := .Messages }}
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{{- $last := eq (len (slice $.Messages $i)) 1 -}}
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{{- if eq .Role "user" }}<|im_start|>user
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{{ .Content }}<|im_end|>
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{{ else if eq .Role "assistant" }}<|im_start|>assistant
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{{ if .Content }}{{ .Content }}
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{{- else if .ToolCalls }}<tool_call>
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{{ range .ToolCalls }}{"name": "{{ .Function.Name }}", "arguments": {{ .Function.Arguments }}}
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{{ end }}</tool_call>
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{{- end }}{{ if not $last }}<|im_end|>
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{{ end }}
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{{- else if eq .Role "tool" }}<|im_start|>user
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<tool_response>
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{{ .Content }}
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</tool_response><|im_end|>
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{{ end }}
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{{- if and (ne .Role "assistant") $last }}<|im_start|>assistant
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{{ end }}
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{{- end }}
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{{- else }}
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{{- if .System }}<|im_start|>system
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{{ .System }}<|im_end|>
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{{ end }}{{ if .Prompt }}<|im_start|>user
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{{ .Prompt }}<|im_end|>
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{{ end }}<|im_start|>assistant
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{{ end }}{{ .Response }}{{ if .Response }}<|im_end|>{{ end }}"""
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PARAMETER stop "<|im_end|>"
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PARAMETER stop "<|im_start|>"
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PARAMETER temperature 0.6
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PARAMETER min_p 0.0
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PARAMETER top_k 20
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PARAMETER top_p 0.95
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PARAMETER repeat_penalty 1
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Qwen3-14B-Gemini-3-Pro-Preview-Distill.bf16.gguf
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oid sha256:df7ac3d37cdeaad0176ccd0b340965e1c118c78f82e5cc399bfdb13c1ca09eaf
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size 29543424480
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oid sha256:df02594508a577ea01e99317231b1a844f455fbac8dc4e317ebd9122771d7a92
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size 8597069280
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oid sha256:7a38fb51d9c2e052b13eab474c4524934b5390ae5201fa17d1aaad039a8fea85
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size 7321313760
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size 9001754080
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Qwen3-14B-Gemini-3-Pro-Preview-Distill.q8_0.gguf
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oid sha256:fc96d145e88a1b4dd50dd01f7e6cc5a383b6e7d5233a93751de27aeffdfe5f03
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size 15698534880
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README.md
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| 1 |
+
---
|
| 2 |
+
tags:
|
| 3 |
+
- gguf
|
| 4 |
+
- llama.cpp
|
| 5 |
+
- unsloth
|
| 6 |
+
datasets:
|
| 7 |
+
- TeichAI/gemini-3-pro-preview-high-reasoning-1000x
|
| 8 |
+
base_model:
|
| 9 |
+
- TeichAI/Qwen3-14B-Gemini-3-Pro-Preview-High-Reasoning-Distill
|
| 10 |
+
---
|
| 11 |
+
|
| 12 |
+
# Qwen3 14B Gemini 3 Pro Preview Reasoning Distill
|
| 13 |
+
|
| 14 |
+
This model was trained on a **Gemini 3 Pro Preview** dataset with a high reasoning effort.
|
| 15 |
+
|
| 16 |
+
- 🤖 Related Models:
|
| 17 |
+
| Model | Effective parameters | Active parameters |
|
| 18 |
+
| ------------- | ------------- | ------------- |
|
| 19 |
+
| [`TeichAI/Qwen3-8B-Gemini-3-Pro-Preview-Distill-GGUF`](https://huggingface.co/TeichAI/Qwen3-8B-Gemini-3-Pro-Preview-Distill-GGUF) | 8 B | 8 B |
|
| 20 |
+
| [`TeichAI/Qwen3-4B-Thinking-2507-Gemini-3-Pro-Preview-High-Reasoning-Distill-GGUF`](https://huggingface.co/TeichAI/Qwen3-4B-Thinking-2507-Gemini-3-Pro-Preview-High-Reasoning-Distill-GGUF) | 4 B | 4 B |
|
| 21 |
+
|
| 22 |
+
|
| 23 |
+
- 🧬 Datasets:
|
| 24 |
+
- `TeichAI/gemini-3-pro-preview-high-reasoning-1000x`
|
| 25 |
+
|
| 26 |
+
- 🏗 Base Model:
|
| 27 |
+
- `unsloth/Qwen3-14B`
|
| 28 |
+
|
| 29 |
+
- ⚡ Use cases:
|
| 30 |
+
- Coding
|
| 31 |
+
- Science
|
| 32 |
+
|
| 33 |
+
- ∑ Stats (Dataset)
|
| 34 |
+
- Costs: $ 32.7 (USD)
|
| 35 |
+
- Total tokens (input + output): 2.73 M
|
| 36 |
+
|
| 37 |
+
---
|
| 38 |
+
|
| 39 |
+
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
|
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|
| 1 |
+
{
|
| 2 |
+
"architectures": [
|
| 3 |
+
"Qwen3ForCausalLM"
|
| 4 |
+
],
|
| 5 |
+
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|
| 6 |
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|
| 7 |
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|
| 8 |
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|
| 9 |
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|
| 10 |
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"hidden_act": "silu",
|
| 11 |
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"hidden_size": 5120,
|
| 12 |
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|
| 13 |
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|
| 14 |
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"layer_types": [
|
| 15 |
+
"full_attention",
|
| 16 |
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"full_attention",
|
| 17 |
+
"full_attention",
|
| 18 |
+
"full_attention",
|
| 19 |
+
"full_attention",
|
| 20 |
+
"full_attention",
|
| 21 |
+
"full_attention",
|
| 22 |
+
"full_attention",
|
| 23 |
+
"full_attention",
|
| 24 |
+
"full_attention",
|
| 25 |
+
"full_attention",
|
| 26 |
+
"full_attention",
|
| 27 |
+
"full_attention",
|
| 28 |
+
"full_attention",
|
| 29 |
+
"full_attention",
|
| 30 |
+
"full_attention",
|
| 31 |
+
"full_attention",
|
| 32 |
+
"full_attention",
|
| 33 |
+
"full_attention",
|
| 34 |
+
"full_attention",
|
| 35 |
+
"full_attention",
|
| 36 |
+
"full_attention",
|
| 37 |
+
"full_attention",
|
| 38 |
+
"full_attention",
|
| 39 |
+
"full_attention",
|
| 40 |
+
"full_attention",
|
| 41 |
+
"full_attention",
|
| 42 |
+
"full_attention",
|
| 43 |
+
"full_attention",
|
| 44 |
+
"full_attention",
|
| 45 |
+
"full_attention",
|
| 46 |
+
"full_attention",
|
| 47 |
+
"full_attention",
|
| 48 |
+
"full_attention",
|
| 49 |
+
"full_attention",
|
| 50 |
+
"full_attention",
|
| 51 |
+
"full_attention",
|
| 52 |
+
"full_attention",
|
| 53 |
+
"full_attention",
|
| 54 |
+
"full_attention"
|
| 55 |
+
],
|
| 56 |
+
"max_length": 8192,
|
| 57 |
+
"max_position_embeddings": 40960,
|
| 58 |
+
"max_window_layers": 40,
|
| 59 |
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"model_type": "qwen3",
|
| 60 |
+
"num_attention_heads": 40,
|
| 61 |
+
"num_hidden_layers": 40,
|
| 62 |
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"num_key_value_heads": 8,
|
| 63 |
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"pad_token_id": 151654,
|
| 64 |
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"rms_norm_eps": 1e-06,
|
| 65 |
+
"rope_scaling": null,
|
| 66 |
+
"rope_theta": 1000000,
|
| 67 |
+
"sliding_window": null,
|
| 68 |
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"tie_word_embeddings": false,
|
| 69 |
+
"transformers_version": "4.56.2",
|
| 70 |
+
"unsloth_fixed": true,
|
| 71 |
+
"unsloth_version": "2025.11.3",
|
| 72 |
+
"use_cache": true,
|
| 73 |
+
"use_sliding_window": false,
|
| 74 |
+
"vocab_size": 151936
|
| 75 |
+
}
|
qwen3-14b.F16.gguf
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:c1cb62014d7afd9c327ddf51dffae77971523beaa1f4316a27eac00eb7563d7e
|
| 3 |
+
size 29543424000
|
qwen3-14b.Q8_0.gguf
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:a84b5ad340f6fe357f4118b5e86444d82d7ced7fb27a7144793eff576ae3c74b
|
| 3 |
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size 15698534400
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