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Model: intellectlabs/Kepler-8B-Instruct-v2 Source: Original Platform
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117
README.md
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README.md
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---
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license: apache-2.0
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base_model: intellectlabs/Kepler-8B-Instruct-v2
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tags:
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- gguf
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- llama.cpp
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- quantized
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- merge
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- mergekit
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- qwen2
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- text-generation
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- conversational
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language:
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- en
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pipeline_tag: text-generation
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---
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<div align="center">
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# 🪐 Kepler-8B-Instruct — GGUF
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### Quantized GGUF builds of Kepler-8B-Instruct — ready to run anywhere llama.cpp does.
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**License: Apache 2.0** · **Format: GGUF** · **Base: [Kepler-8B-Instruct](https://huggingface.co/intellectlabs/Kepler-8B-Instruct)**
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**[Original Model](https://huggingface.co/intellectlabs/Kepler-8B-Instruct)** · **[llama.cpp](https://github.com/ggerganov/llama.cpp)** · **[Discussions](../../discussions)**
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</div>
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---
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## ✨ What is this?
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**Kepler-8B-Instruct** is an 8-billion-parameter assistant built by merging `Qwen/Qwen2.5-7B-Instruct` (85%) with `deepseek-ai/DeepSeek-R1-Distill-Qwen-7B` (15%) using [mergekit](https://github.com/arcee-ai/mergekit). The blend keeps Qwen2.5-Instruct's clean, reliable instruction-following as the foundation while folding in a touch of DeepSeek's reasoning-distilled behavior.
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This repository packages that model as **GGUF** — the universal format for [llama.cpp](https://github.com/ggerganov/llama.cpp) and everything built on it. One file, no Python environment, no CUDA setup. It runs on a gaming laptop, a Raspberry Pi-class board, or a headless server with equal ease.
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**Highlights:**
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- 🧩 A reliable instruction-following foundation with a light touch of reasoning-model flavor
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- ⚡ Seven precision levels included, from a ~3 GB ultra-light build to full-precision F16
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- 🔌 Drop-in compatible with llama.cpp, LM Studio, Ollama, koboldcpp, GPT4All, Jan, and text-generation-webui
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- 🗂️ Native ChatML support, with a built-in default identity — ask "who are you" and it answers as Kepler by intellectlabs, in whatever language you ask in
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## 🚀 Quick Start
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### llama.cpp
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```bash
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./llama-cli -hf intellectlabs/Kepler-8B-Instruct-GGUF:Q4_K_M -p "Explain quantum entanglement simply."
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```
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Or download a specific file manually:
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```bash
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huggingface-cli download intellectlabs/Kepler-8B-Instruct-GGUF Kepler-8B-Instruct-Q4_K_M.gguf --local-dir .
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./llama-cli -m Kepler-8B-Instruct-Q4_K_M.gguf -p "Hello, who are you?" -cnv
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```
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### LM Studio
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Search **`intellectlabs/Kepler-8B-Instruct-GGUF`** directly in the LM Studio model search bar and download your preferred quant.
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### Ollama
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```bash
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ollama run hf.co/intellectlabs/Kepler-8B-Instruct-GGUF:Q4_K_M
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```
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## 📦 Available Quantizations
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| File | Quant | Size (approx.) | Quality | Recommended For |
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|---|---|---|---|---|
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| `Kepler-8B-Instruct-Q2_K.gguf` | Q2_K | ~3.1 GB | ⭐️ | Extreme low-RAM devices only |
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| `Kepler-8B-Instruct-Q3_K_M.gguf` | Q3_K_M | ~4.0 GB | ⭐️⭐️ | Low-RAM systems, testing |
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| `Kepler-8B-Instruct-Q4_0.gguf` | Q4_0 | ~4.7 GB | ⭐️⭐️⭐️ | Legacy-hardware compatibility |
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| `Kepler-8B-Instruct-Q4_K_M.gguf` | Q4_K_M | ~4.9 GB | ⭐️⭐️⭐️⭐️ | **Best balance — recommended default** |
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| `Kepler-8B-Instruct-Q5_K_M.gguf` | Q5_K_M | ~5.7 GB | ⭐️⭐️⭐️⭐️ | Higher quality, still efficient |
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| `Kepler-8B-Instruct-Q8_0.gguf` | Q8_0 | ~8.5 GB | ⭐️⭐️⭐️⭐️⭐️ | Near-lossless, if you have the RAM |
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| `Kepler-8B-Instruct-F16.gguf` | F16 | ~16 GB | ⭐️⭐️⭐️⭐️⭐️ | Full precision, GPU/server use |
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> 💡 **New here?** Start with **Q4_K_M** — it's the sweet spot most people use: small enough to run comfortably, strong enough to feel close to the full model.
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## 🧠 Prompt Format
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This model uses the **ChatML** template (inherited from its Qwen2 base):
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```
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<|im_start|>system
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You are a helpful assistant.<|im_end|>
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<|im_start|>user
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{prompt}<|im_end|>
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<|im_start|>assistant
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```
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Most tools (llama.cpp `-cnv`, LM Studio, Ollama) apply this automatically via the embedded chat template — no manual formatting needed.
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## 🛠️ How It Was Made
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1. **Merge** — `deepseek-ai/DeepSeek-R1-Distill-Qwen-7B` linearly blended into `Qwen/Qwen2.5-7B-Instruct` (15%/85%) using mergekit, with the tokenizer taken from the base model.
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2. **Convert** — merged safetensors converted to GGUF (F16) using `llama.cpp`'s `convert_hf_to_gguf.py`.
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3. **Quantize** — F16 GGUF quantized into multiple formats using `llama-quantize`, covering everything from ultra-compact (Q2_K) to near-lossless (Q8_0).
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## ⚠️ Limitations
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- This is a **merge**, not a model trained from scratch — behavior is a blend of its parent models and may inherit their quirks or biases.
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- Lower-bit quantizations (Q2_K, Q3_K_M) trade quality for size; expect more inconsistency at extreme compression.
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- Not evaluated on standard benchmarks yet — treat outputs with normal LLM caution, especially for factual or high-stakes use.
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## 🙏 Credits
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- Base models: [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct), [deepseek-ai/DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B)
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- Merge tooling: [mergekit](https://github.com/arcee-ai/mergekit)
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- Quantization tooling: [llama.cpp](https://github.com/ggerganov/llama.cpp)
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---
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||||
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<div align="center">
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**If this was useful, a ⭐️ on the repo helps others find it.**
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</div>
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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{%- else %}
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{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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{%- endif %}
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{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0]['role'] == 'system' %}
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{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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{%- else %}
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{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '\n<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- endif %}
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{
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"architectures": [
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"Qwen2ForCausalLM"
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention"
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],
|
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"max_position_embeddings": 32768,
|
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"max_window_layers": 28,
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"model_type": "qwen2",
|
||||
"num_attention_heads": 28,
|
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|
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"num_key_value_heads": 4,
|
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|
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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|
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"rope_type": "default"
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|
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"sliding_window": null,
|
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"tie_word_embeddings": false,
|
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"transformers_version": "5.0.0",
|
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"use_cache": true,
|
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"use_sliding_window": false,
|
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"vocab_size": 151665
|
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}
|
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mergekit_config.yml
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mergekit_config.yml
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slices:
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||||
- sources:
|
||||
- model: Qwen/Qwen2.5-7B-Instruct
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layer_range: [0, 28]
|
||||
- model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
|
||||
layer_range: [0, 28]
|
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merge_method: slerp
|
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base_model: Qwen/Qwen2.5-7B-Instruct
|
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parameters:
|
||||
t:
|
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- value: 0.5
|
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dtype: bfloat16
|
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tokenizer_source: base
|
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||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
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oid sha256:3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
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size 11421892
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30
tokenizer_config.json
Normal file
30
tokenizer_config.json
Normal file
@@ -0,0 +1,30 @@
|
||||
{
|
||||
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||||
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||||
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"chat_template": "{%- if messages[0]['role'] != 'system' -%}{{- '<|im_start|>system\\nYou are Kepler, an AI assistant created by intellectlabs. If asked who you are, who made you, or what model you are, give a short, direct one- or two-sentence answer \u2014 you are Kepler by intellectlabs. Never claim to be Qwen, DeepSeek, ChatGPT, or any other assistant. Do not add extra unrelated commentary, examples, or continue the conversation on your own \u2014 answer only what was asked, then stop. Always reply in the same language the user wrote in (e.g. reply in Hindi if asked in Hindi, English if asked in English). Be helpful, clear, and concise, like a professional assistant.<|im_end|>\\n' -}}{%- endif -%}{%- for message in messages -%}{{- '<|im_start|>' + message['role'] + '\\n' + message['content'] + '<|im_end|>\\n' -}}{%- endfor -%}{%- if add_generation_prompt -%}{{- '<|im_start|>assistant\\n' -}}{%- endif -%}"
|
||||
}
|
||||
Reference in New Issue
Block a user