output = llm(
"Once upon a time,",
max_tokens=512,
echo=True
)
print(output)🍌 BananaMind V1
⚠️ Honest disclaimer: This model is not very good. It's a small experimental model trained as a personal project. Manage your expectations accordingly.
BananaMind V1 is a 125M parameter causal language model fine-tuned on conversational data. It is uploaded in GGUF format for use with llama.cpp and compatible runtimes.
Model Details
| Property | Value |
|---|---|
| Model Name | BananaMind V1 |
| Parameters | 125M |
| Context Length | 512 tokens |
| Format | GGUF |
| License | Apache 2.0 |
| Language | English |
Training Data
| Dataset | License |
|---|---|
| OpenAssistant/oasst1 | Apache 2.0 |
Intended Use
- Experimenting with small language models
- Local inference on CPU via llama.cpp
- Educational / research purposes
- Tinkering and testing
Not intended for: Production use, safety-critical applications, or anything requiring reliable or accurate outputs.
How to Use
With llama.cpp
./llama-cli -m BananaMind-V1.gguf \
-p "User: Hello! How are you?\nAssistant:" \
-n 128 \
--ctx-size 512
With Python (via llama-cpp-python)
from llama_cpp import Llama
llm = Llama(
model_path="BananaMind-V1.gguf",
n_ctx=512,
)
output = llm(
"User: What is the capital of France?\nAssistant:",
max_tokens=128,
stop=["User:"],
)
print(output["choices"][0]["text"])
With Ollama
ollama run banaxitech/bananamind-v1
``
---
## Limitations & Known Issues
- **Small capacity:** 125M parameters is quite limited — don't expect GPT-4 quality.
- **Short context:** 512 token context window means it forgets things quickly.
- **Hallucinations:** Will confidently make things up.
- **Inconsistency:** Outputs can vary wildly between runs.
- **Generally not very good** — as stated upfront. Consider this a starting point, not a destination.
---
## Evaluation
No formal benchmarks were run. Informal testing suggests the model can hold basic conversations but struggles with anything requiring reasoning, factual recall, or multi-turn coherence.
---
## License
This model is released under the **Apache 2.0** license, consistent with its training data. See [LICENSE](https://www.apache.org/licenses/LICENSE-2.0) for details.
---
## Citation
If you somehow use this model in research (why?), you can cite it informally as:
BananaMind V1 (2024). A small 125M parameter GGUF language model. Personal project.
---
*Made with 🍌 and questionable judgment.*
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# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Banaxi-Tech/BananaMind-v1", filename="BananaMind-V1.gguf", )