The models `gemma3:12b`, `dolphin-llama3:8b`, and `llama2-uncensored:7b`— are primarily designed for text generation tasks (e.g., chat, completion) rather than sentence embeddings. They are not optimized for sentence transformation (i.e., generating dense vector representations for semantic similarity, clustering, or Retrieval-Augmented Generation). These models produce natural language outputs and lack the contrastive training or architecture (e.g., transformer-based sentence encoders) needed for high-quality sentence embeddings.
However, you can still use these models indirectly for sentence transformation tasks by generating embeddings through a process called "prompt-based embedding" or by leveraging their hidden states. This is less efficient and typically less accurate than using a dedicated embedding model like `all-minilm` (Ollama's quantized `all-MiniLM-L6-v2`). Below, I’ll explain why generative models aren’t ideal, outline how you could use them for embeddings if needed, and recommend integrating `all-minilm` as a use case.
### Why these Models Aren’t Suitable for Sentence Transformation
1. **Architecture and Training**:
- `gemma3:12b`, `dolphin-llama3:8b`, and `llama2-uncensored:7b` are autoregressive language models optimized for generating text, not for producing fixed-size vector embeddings.
- Sentence transformation models like `all-MiniLM-L6-v2` are trained with contrastive learning (e.g., on sentence pairs) to map semantically similar sentences to nearby points in a vector space (384 dimensions for `all-minilm`). Generative models lack this training.
- Output: These models generate token sequences, not fixed-length embeddings. Extracting embeddings (e.g., from hidden states) is possible but suboptimal.
2. **Performance and Efficiency**:
- Those models are large (`gemma3:12b` ~12B parameters, `dolphin-llama3:8b` ~8B, `llama2-uncensored:7b` ~7B), requiring significant RAM (4-16 GB) and potentially GPU acceleration for reasonable speed.
- `all-minilm` (~22M parameters, ~80 MB) is far more efficient, processing ~14k sentences/sec on CPU with ~100-200 MB RAM, compared to generative models’ slower inference and higher resource demands.
3. **Accuracy**:
- Dedicated embedding models like `all-MiniLM-L6-v2` achieve high accuracy on benchmarks like STS-B (~84-85% Spearman correlation for semantic similarity).
- Embeddings from generative models (e.g., via pooling hidden states) typically score lower (~60-70% on STS-B) due to lack of task-specific fine-tuning.
### Can these Models be used for Sentence Transformation?
Yes, but it’s a workaround. You can extract embeddings from these models by:
1. **Using Hidden States**: Pass a sentence through the model and average the hidden state outputs (e.g., last layer) to create a fixed-size vector.
2. **Prompt-Based Similarity**: Use the model to score sentence pairs for similarity via prompts, though this is slow and not scalable.
Here’s an example of generating embeddings using `gemma3:12b` in Ollama (adaptable for `dolphin-llama3:8b` or `llama2-uncensored:7b`):
**Python Code (Hidden State Embeddings):**
```python
import ollama
import numpy as np
def get_embedding(sentence, model="gemma3:12b"):
# Ollama's API doesn't expose hidden states directly, so we use generate with embeddings
response = ollama.embeddings(model=model, prompt=sentence)
# Note: This assumes the model supports embeddings; if not, fallback to generation
try:
embedding = response['embedding']
except KeyError:
# Fallback: Generate and simulate embedding (not ideal)
response = ollama.generate(model=model, prompt=sentence, options={"return_embeddings": True})
embedding = np.random.rand(768) # Placeholder; actual hidden state extraction needs custom setup
return embedding
sentence = "Llamas are members of the camelid family"
embedding = get_embedding(sentence, model="gemma3:12b")
print(f"Embedding length: {len(embedding)}")
```
**Caveats**:
- **Ollama Limitation**: Ollama’s API doesn’t natively expose hidden states for these models. The above code assumes a hypothetical embedding endpoint. In practice, you’d need to modify the model (e.g., via Hugging Face) to extract hidden states, which requires significant setup (see below).
- **Accuracy**: Expect lower quality (e.g., ~60-70% STS-B) compared to `all-minilm` (~84-85%).
- **Resources**: `gemma3:12b` needs ~16 GB RAM; `dolphin-llama3:8b` and `llama2-uncensored:7b` need ~8-12 GB, vs. ~100-200 MB for `all-minilm`.
### Recommended Approach: Use all-minilm
For using `all-MiniLM-L6-v2`, the best approach is to use Ollama’s `all-minilm` for sentence transformation. It’s lightweight, accurate, and directly supported. Here’s how to integrate it:
1. **Pull all-minilm**:
```
ollama pull all-minilm
```
Verify: `ollama list` (should show `all-minilm:latest`).
2. **Reuse Previous Examples**:
Use the code from my prior response for embeddings or RAG. For example, the RAG pipeline with `all-minilm` (and one of the generative models for generation):
**Python Code (RAG with all-minilm and gemma3:12b):**
```python
import ollama
import chromadb
# Sample documents
documents = [
"Llamas are members of the camelid family meaning they're pretty closely related to vicuñas and camels",
"Llamas were first domesticated and used as pack animals 4,000 to 5,000 years ago in the Peruvian highlands",
"Llamas can grow as much as 6 feet tall though the average llama between 5 feet 6 inches and 5 feet 9 inches tall",
]
# Set up ChromaDB
client = chromadb.Client()
collection = client.create_collection(name="llama_docs")
# Embed with all-minilm
for i, doc in enumerate(documents):
response = ollama.embed(model="all-minilm", input=doc)
collection.add(
ids=[str(i)],
embeddings=[response['embedding']], # 384-dim from all-MiniLM-L6-v2
documents=[doc]
)
# Query
query = "What animals are llamas related to?"
query_response = ollama.embed(model="all-minilm", input=query)
results = collection.query(
query_embeddings=[query_response['embedding']],
n_results=1
)
relevant_doc = results['documents'][0][0]
print(f"Relevant document: {relevant_doc}")
# Generate response with gemma3:12b
prompt = f"Using this data: {relevant_doc}. Respond to: {query}"
output = ollama.generate(model="gemma3:12b", prompt=prompt)
print(f"Generated response: {output['response']}")
```
- **Run**: Save as `rag.py`, ensure `all-minilm` and `gemma3:12b` are pulled, and run `python rag.py`.
- **Why It Works**: `all-minilm` handles embeddings efficiently (384 dims, ~100-200 MB RAM, ~14k sentences/sec). `gemma3:12b` (or `dolphin-llama3:8b`, `llama2-uncensored:7b`) generates coherent responses from retrieved documents.
### Comparison of some generative Models
| Model | Suitable for Embeddings? | RAM (est.) | CPU Needed | Tokens (max) | Accuracy (STS-B, est.) |
|----------------------|--------------------------|------------|------------|--------------|-----------------------|
| **all-minilm** | Yes (designed for it) | ~100-200 MB | Low | 256 | ~84-85% |
| **gemma3:12b** | No (workaround possible) | ~16 GB | High (GPU preferred) | ~4096 | ~60-70% (workaround) |
| **dolphin-llama3:8b**| No (workaround possible) | ~12 GB | Medium-High | ~8192 | ~60-70% (workaround) |
| **llama2-uncensored:7b** | No (workaround possible) | ~8-12 GB | Medium-High | ~4096 | ~60-70% (workaround) |
### Notes
- **Recommendation**: Stick with `all-minilm` for sentence transformation. The generative models are better for generating responses in RAG (as shown above) than creating embeddings.
- **If You Must Use Generative Models**: Extracting hidden states requires exporting the model to Hugging Face format, modifying it to output embeddings, and re-importing to Ollama (complex; not recommended). Example for `llama2-uncensored:7b`:
1. Convert to Hugging Face format (if available).
2. Use `transformers` to extract last hidden states:
```python
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("path/to/llama2")
model = AutoModel.from_pretrained("path/to/llama2")
inputs = tokenizer("Llamas are cool", return_tensors="pt")
outputs = model(**inputs)
embedding = outputs.last_hidden_state.mean(dim=1).squeeze().detach().numpy()
```
3. Re-import to Ollama (requires ONNX conversion, as in my prior response).
- **Next Steps**: Pull `all-minilm` and use the RAG code above.