Instructions to use RichardErkhov/icefog72_-_IceTeaRP-7b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RichardErkhov/icefog72_-_IceTeaRP-7b-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="RichardErkhov/icefog72_-_IceTeaRP-7b-gguf",
	filename="IceTeaRP-7b.IQ3_M.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 RichardErkhov/icefog72_-_IceTeaRP-7b-gguf 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 RichardErkhov/icefog72_-_IceTeaRP-7b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RichardErkhov/icefog72_-_IceTeaRP-7b-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf RichardErkhov/icefog72_-_IceTeaRP-7b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RichardErkhov/icefog72_-_IceTeaRP-7b-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 RichardErkhov/icefog72_-_IceTeaRP-7b-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf RichardErkhov/icefog72_-_IceTeaRP-7b-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 RichardErkhov/icefog72_-_IceTeaRP-7b-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RichardErkhov/icefog72_-_IceTeaRP-7b-gguf:Q4_K_M

Use Docker

docker model run hf.co/RichardErkhov/icefog72_-_IceTeaRP-7b-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use RichardErkhov/icefog72_-_IceTeaRP-7b-gguf with Ollama:
```
ollama run hf.co/RichardErkhov/icefog72_-_IceTeaRP-7b-gguf:Q4_K_M
```

Unsloth Studio

How to use RichardErkhov/icefog72_-_IceTeaRP-7b-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 RichardErkhov/icefog72_-_IceTeaRP-7b-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 RichardErkhov/icefog72_-_IceTeaRP-7b-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for RichardErkhov/icefog72_-_IceTeaRP-7b-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use RichardErkhov/icefog72_-_IceTeaRP-7b-gguf with Docker Model Runner:
```
docker model run hf.co/RichardErkhov/icefog72_-_IceTeaRP-7b-gguf:Q4_K_M
```

Lemonade

How to use RichardErkhov/icefog72_-_IceTeaRP-7b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RichardErkhov/icefog72_-_IceTeaRP-7b-gguf:Q4_K_M

Run and chat with the model

lemonade run user.icefog72_-_IceTeaRP-7b-gguf-Q4_K_M

List all available models

lemonade list

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Quantization made by Richard Erkhov.

Github

Discord

Request more models

IceTeaRP-7b - GGUF

Model creator: https://huggingface.co/icefog72/
Original model: https://huggingface.co/icefog72/IceTeaRP-7b/

Name	Quant method	Size
IceTeaRP-7b.Q2_K.gguf	Q2_K	2.53GB
IceTeaRP-7b.IQ3_XS.gguf	IQ3_XS	2.81GB
IceTeaRP-7b.IQ3_S.gguf	IQ3_S	2.96GB
IceTeaRP-7b.Q3_K_S.gguf	Q3_K_S	2.95GB
IceTeaRP-7b.IQ3_M.gguf	IQ3_M	3.06GB
IceTeaRP-7b.Q3_K.gguf	Q3_K	3.28GB
IceTeaRP-7b.Q3_K_M.gguf	Q3_K_M	3.28GB
IceTeaRP-7b.Q3_K_L.gguf	Q3_K_L	3.56GB
IceTeaRP-7b.IQ4_XS.gguf	IQ4_XS	3.67GB
IceTeaRP-7b.Q4_0.gguf	Q4_0	3.83GB
IceTeaRP-7b.IQ4_NL.gguf	IQ4_NL	3.87GB
IceTeaRP-7b.Q4_K_S.gguf	Q4_K_S	3.86GB
IceTeaRP-7b.Q4_K.gguf	Q4_K	4.07GB
IceTeaRP-7b.Q4_K_M.gguf	Q4_K_M	4.07GB
IceTeaRP-7b.Q4_1.gguf	Q4_1	4.24GB
IceTeaRP-7b.Q5_0.gguf	Q5_0	4.65GB
IceTeaRP-7b.Q5_K_S.gguf	Q5_K_S	4.65GB
IceTeaRP-7b.Q5_K.gguf	Q5_K	4.78GB
IceTeaRP-7b.Q5_K_M.gguf	Q5_K_M	4.78GB
IceTeaRP-7b.Q5_1.gguf	Q5_1	5.07GB
IceTeaRP-7b.Q6_K.gguf	Q6_K	5.53GB
IceTeaRP-7b.Q8_0.gguf	Q8_0	7.17GB

Original model description:

base_model: - icefog72/Kunokukulemonchini-7b - icefog72/BigLM7-7b - liminerity/M7-7b - Undi95/BigL-7B library_name: transformers tags: - mergekit - merge - alpaca - mistral - not-for-all-audiences - nsfw license: cc-by-nc-4.0 model-index: - name: IceTeaRP-7b results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 66.98 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=icefog72/IceTeaRP-7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 86.13 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=icefog72/IceTeaRP-7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 63.97 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=icefog72/IceTeaRP-7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 62.44 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=icefog72/IceTeaRP-7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 78.85 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=icefog72/IceTeaRP-7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 60.20 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=icefog72/IceTeaRP-7b name: Open LLM Leaderboard

Check out new merge model without repetition problem IceLemonTeaRP-32k-7b

IceTeaRP-7b

This is a merge of pre-trained language models created using mergekit.

Thanks mradermacher for IceTeaRP-7b-GGUF

Merge Details

Just cooking mergers. For my taste, it came out better than Kunokukulemonchini-7b. Model capable of handling 32k context window without any scaling.

Prompt template: Alpaca

measurement.json for quanting exl2 included.

Users test feedback

Can develop repetition problem at 16k-32k without good RP rules/CoT in promt.

You can try edit "rope_theta": 100000.0 => "rope_theta": 60000.0 to make it even more slightly coherent(hard to find balance).

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

icefog72/Kunokukulemonchini-7b
BigLM7-7b SLERP merge of
- liminerity/M7-7b
- Undi95/BigL-7B

How to download From the command line

I recommend using the huggingface-hub Python library:

pip3 install huggingface-hub

To download the main branch to a folder called IceTeaRP-7b:

mkdir IceTeaRP-7b
huggingface-cli download icefog72/IceTeaRP-7b --local-dir IceTeaRP-7b --local-dir-use-symlinks False

More advanced huggingface-cli download usage

If you remove the --local-dir-use-symlinks False parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: ~/.cache/huggingface), and symlinks will be added to the specified --local-dir, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.

The cache location can be changed with the HF_HOME environment variable, and/or the --cache-dir parameter to huggingface-cli.

For more documentation on downloading with huggingface-cli, please see: HF -> Hub Python Library -> Download files -> Download from the CLI.

To accelerate downloads on fast connections (1Gbit/s or higher), install hf_transfer:

pip3 install hf_transfer

And set environment variable HF_HUB_ENABLE_HF_TRANSFER to 1:

mkdir FOLDERNAME
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download MODEL --local-dir FOLDERNAME --local-dir-use-symlinks False

Windows Command Line users: You can set the environment variable by running set HF_HUB_ENABLE_HF_TRANSFER=1 before the download command.

Configuration

The following YAML configuration was used to produce this model:


slices:
  - sources:
      - model: Kunokukulemonchini-7b
        layer_range: [0, 32]
      - model: BigLM7-7b
        layer_range: [0, 32]
merge_method: slerp
base_model: Kunokukulemonchini-7b
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: float16

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	69.76
AI2 Reasoning Challenge (25-Shot)	66.98
HellaSwag (10-Shot)	86.13
MMLU (5-Shot)	63.97
TruthfulQA (0-shot)	62.44
Winogrande (5-shot)	78.85
GSM8k (5-shot)	60.20

Downloads last month: 172

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

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RichardErkhov
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icefog72_-_IceTeaRP-7b-gguf