Instructions to use LoneStriker/gemma-7b-it-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LoneStriker/gemma-7b-it-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("LoneStriker/gemma-7b-it-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use LoneStriker/gemma-7b-it-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 LoneStriker/gemma-7b-it-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf LoneStriker/gemma-7b-it-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 LoneStriker/gemma-7b-it-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf LoneStriker/gemma-7b-it-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 LoneStriker/gemma-7b-it-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf LoneStriker/gemma-7b-it-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 LoneStriker/gemma-7b-it-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf LoneStriker/gemma-7b-it-GGUF:Q4_K_M
Use Docker
docker model run hf.co/LoneStriker/gemma-7b-it-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use LoneStriker/gemma-7b-it-GGUF with Ollama:
ollama run hf.co/LoneStriker/gemma-7b-it-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use LoneStriker/gemma-7b-it-GGUF with Docker Model Runner:
docker model run hf.co/LoneStriker/gemma-7b-it-GGUF:Q4_K_M
- Lemonade
How to use LoneStriker/gemma-7b-it-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull LoneStriker/gemma-7b-it-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.gemma-7b-it-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Upload folder using huggingface_hub
Browse files- .gitattributes +0 -4
- README.md +4 -12
- gemma-7b-it-Q3_K_L.gguf +2 -2
- gemma-7b-it-Q4_K_M.gguf +2 -2
- gemma-7b-it-Q5_K_M.gguf +2 -2
.gitattributes
CHANGED
|
@@ -1,9 +1,5 @@
|
|
| 1 |
gemma-7b-it-Q3_K_L.gguf filter=lfs diff=lfs merge=lfs -text
|
| 2 |
-
gemma-7b-it-Q3_K_M.gguf filter=lfs diff=lfs merge=lfs -text
|
| 3 |
-
gemma-7b-it-Q3_K_S.gguf filter=lfs diff=lfs merge=lfs -text
|
| 4 |
gemma-7b-it-Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
|
| 5 |
-
gemma-7b-it-Q4_K_S.gguf filter=lfs diff=lfs merge=lfs -text
|
| 6 |
gemma-7b-it-Q5_K_M.gguf filter=lfs diff=lfs merge=lfs -text
|
| 7 |
-
gemma-7b-it-Q5_K_S.gguf filter=lfs diff=lfs merge=lfs -text
|
| 8 |
gemma-7b-it-Q6_K.gguf filter=lfs diff=lfs merge=lfs -text
|
| 9 |
gemma-7b-it-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
|
|
|
|
| 1 |
gemma-7b-it-Q3_K_L.gguf filter=lfs diff=lfs merge=lfs -text
|
|
|
|
|
|
|
| 2 |
gemma-7b-it-Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
|
|
|
|
| 3 |
gemma-7b-it-Q5_K_M.gguf filter=lfs diff=lfs merge=lfs -text
|
|
|
|
| 4 |
gemma-7b-it-Q6_K.gguf filter=lfs diff=lfs merge=lfs -text
|
| 5 |
gemma-7b-it-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
|
README.md
CHANGED
|
@@ -1,14 +1,6 @@
|
|
| 1 |
---
|
| 2 |
library_name: transformers
|
| 3 |
tags: []
|
| 4 |
-
widget:
|
| 5 |
-
- text: |
|
| 6 |
-
<start_of_turn>user
|
| 7 |
-
How does the brain work?<end_of_turn>
|
| 8 |
-
<start_of_turn>model
|
| 9 |
-
inference:
|
| 10 |
-
parameters:
|
| 11 |
-
max_new_tokens: 200
|
| 12 |
extra_gated_heading: "Access Gemma on Hugging Face"
|
| 13 |
extra_gated_prompt: "To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged-in to Hugging Face and click below. Requests are processed immediately."
|
| 14 |
extra_gated_button_content: "Acknowledge license"
|
|
@@ -27,7 +19,7 @@ This model card corresponds to the 7B instruct version of the Gemma model. You c
|
|
| 27 |
|
| 28 |
* [Responsible Generative AI Toolkit](https://ai.google.dev/responsible)
|
| 29 |
* [Gemma on Kaggle](https://www.kaggle.com/models/google/gemma)
|
| 30 |
-
* [Gemma on Vertex Model Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/335
|
| 31 |
|
| 32 |
**Terms of Use**: [Terms](https://www.kaggle.com/models/google/gemma/license/consent)
|
| 33 |
|
|
@@ -73,9 +65,9 @@ tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b-it")
|
|
| 73 |
model = AutoModelForCausalLM.from_pretrained("google/gemma-7b-it")
|
| 74 |
|
| 75 |
input_text = "Write me a poem about Machine Learning."
|
| 76 |
-
input_ids = tokenizer(input_text, return_tensors="pt")
|
| 77 |
|
| 78 |
-
outputs = model.generate(
|
| 79 |
print(tokenizer.decode(outputs[0]))
|
| 80 |
```
|
| 81 |
|
|
@@ -309,7 +301,7 @@ several advantages in this domain:
|
|
| 309 |
|
| 310 |
### Software
|
| 311 |
|
| 312 |
-
Training was done using [JAX](https://github.com/google/jax) and [ML Pathways](https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture).
|
| 313 |
|
| 314 |
JAX allows researchers to take advantage of the latest generation of hardware,
|
| 315 |
including TPUs, for faster and more efficient training of large models.
|
|
|
|
| 1 |
---
|
| 2 |
library_name: transformers
|
| 3 |
tags: []
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 4 |
extra_gated_heading: "Access Gemma on Hugging Face"
|
| 5 |
extra_gated_prompt: "To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged-in to Hugging Face and click below. Requests are processed immediately."
|
| 6 |
extra_gated_button_content: "Acknowledge license"
|
|
|
|
| 19 |
|
| 20 |
* [Responsible Generative AI Toolkit](https://ai.google.dev/responsible)
|
| 21 |
* [Gemma on Kaggle](https://www.kaggle.com/models/google/gemma)
|
| 22 |
+
* [Gemma on Vertex Model Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/335)
|
| 23 |
|
| 24 |
**Terms of Use**: [Terms](https://www.kaggle.com/models/google/gemma/license/consent)
|
| 25 |
|
|
|
|
| 65 |
model = AutoModelForCausalLM.from_pretrained("google/gemma-7b-it")
|
| 66 |
|
| 67 |
input_text = "Write me a poem about Machine Learning."
|
| 68 |
+
input_ids = tokenizer(**input_text, return_tensors="pt")
|
| 69 |
|
| 70 |
+
outputs = model.generate(input_ids)
|
| 71 |
print(tokenizer.decode(outputs[0]))
|
| 72 |
```
|
| 73 |
|
|
|
|
| 301 |
|
| 302 |
### Software
|
| 303 |
|
| 304 |
+
Training was done using [JAX](https://github.com/google/jax) and [ML Pathways](https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/ml-pathways).
|
| 305 |
|
| 306 |
JAX allows researchers to take advantage of the latest generation of hardware,
|
| 307 |
including TPUs, for faster and more efficient training of large models.
|
gemma-7b-it-Q3_K_L.gguf
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:4e817f0fc6f7c421cb314a9a19c9ed3b0f5474cd800b055b20566988c8496dc6
|
| 3 |
+
size 4709393568
|
gemma-7b-it-Q4_K_M.gguf
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:c92ce72d07ba4f92b46a50f0e7e04d30ba4700dc49d34f4750e9dd366fbbecca
|
| 3 |
+
size 5330085024
|
gemma-7b-it-Q5_K_M.gguf
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:ec8a69c31575b174416d84adc169e8b5969bbe391d52513abfbe8feaa993c001
|
| 3 |
+
size 6144828576
|