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Ollama Integration

If you already use Ollama and have downloaded GGUF models, localLLM can discover and load them directly without re-downloading. This saves disk space and bandwidth by reusing models you’ve already installed.

Discovering Ollama Models

Use list_ollama_models() to see all GGUF models managed by Ollama:

library(localLLM)

models <- list_ollama_models()
print(models)
#>                name      size                                    path
#> 1          llama3.2    2.0 GB ~/.ollama/models/blobs/sha256-6340dc32...
#> 2      deepseek-r1:8b  4.9 GB ~/.ollama/models/blobs/sha256-8a2b7c9e...
#> 3           gemma2:9b  5.4 GB ~/.ollama/models/blobs/sha256-9f8a7b6c...

Loading Ollama Models

You can reference Ollama models in several ways:

By Model Name

model <- model_load("ollama:llama3.2")

By Tag

model <- model_load("ollama:deepseek-r1:8b")

By SHA256 Prefix

# Use at least 8 characters of the SHA256 hash
model <- model_load("ollama:6340dc32")

Interactive Selection

# Lists all models and prompts for selection
model <- model_load("ollama")
#> Available Ollama models:
#> 1. llama3.2 (2.0 GB)
#> 2. deepseek-r1:8b (4.9 GB)
#> 3. gemma2:9b (5.4 GB)
#>
#> Enter selection: 1

Using with quick_llama()

# Use Ollama model with quick_llama
response <- quick_llama(
  "Explain quantum computing in simple terms",
  model_path = "ollama:llama3.2"
)
cat(response)

Ollama Reference Trigger Rules

The model_path parameter triggers Ollama model discovery when it matches specific patterns:

Input Triggers Ollama Description
"ollama" Yes Exact match (case-insensitive)
"Ollama" Yes Case-insensitive
" ollama " Yes Whitespace is trimmed
"ollama:llama3" Yes Starts with ollama:
"ollama:deepseek-r1:8b" Yes Full model name with tag
"ollama:6340dc32" Yes SHA256 prefix (8+ chars recommended)
"myollama" No Not exact match, doesn’t start with ollama:
"ollama.gguf" No Treated as filename, not Ollama reference

Common Workflows

Check Available Models First

# See what's available
available <- list_ollama_models()

if (nrow(available) > 0) {
  cat("Found", nrow(available), "Ollama models:\n")
  print(available[, c("name", "size")])
} else {
  cat("No Ollama models found. Install some with: ollama pull llama3.2\n")
}

Load Specific Model

# Load by exact name
model <- model_load("ollama:llama3.2")

# Create context and generate
ctx <- context_create(model, n_ctx = 4096)

messages <- list(
  list(role = "user", content = "What is machine learning?")
)
prompt <- apply_chat_template(model, messages)
response <- generate(ctx, prompt, max_tokens = 200)
cat(response)

Model Comparison with Ollama

# Compare Ollama models
models <- list(
  list(
    id = "llama3.2",
    model_path = "ollama:llama3.2",
    n_gpu_layers = 999
  ),
  list(
    id = "deepseek",
    model_path = "ollama:deepseek-r1:8b",
    n_gpu_layers = 999
  )
)

# Run comparison
results <- explore(
  models = models,
  prompts = my_prompts,
  engine = "parallel"
)

Ollama Directory Structure

Ollama stores models in a specific location:

The actual GGUF files are stored in:

~/.ollama/models/blobs/sha256-<hash>

localLLM reads the Ollama manifest files to map model names to their blob locations.

Troubleshooting

Model Not Found

model <- model_load("ollama:nonexistent")
#> Error: No Ollama model found matching 'nonexistent'.
#> Available models: llama3.2, deepseek-r1:8b, gemma2:9b

Solution: Check available models with list_ollama_models() and verify the name.

Ollama Not Installed

models <- list_ollama_models()
#> Warning: Ollama directory not found at ~/.ollama/models
#> data frame with 0 columns and 0 rows

Solution: Install Ollama from ollama.com and pull some models:

# In terminal
ollama pull llama3.2
ollama pull gemma2:9b

Multiple Matches

model <- model_load("ollama:llama")
#> Multiple models match 'llama':
#> 1. llama3.2 (2.0 GB)
#> 2. llama2:7b (3.8 GB)
#>
#> Enter selection (or be more specific with model_load("ollama:llama3.2")):

Solution: Use a more specific name or select interactively.

Benefits of Ollama Integration

  1. Save Disk Space: No duplicate downloads
  2. Faster Setup: Use models you’ve already downloaded
  3. Easy Discovery: list_ollama_models() shows what’s available
  4. Flexible References: Multiple ways to specify models
  5. Seamless Integration: Same API as other model sources

Complete Example

library(localLLM)

# 1. Check what's available
available <- list_ollama_models()
print(available)

# 2. Load a model
model <- model_load("ollama:llama3.2", n_gpu_layers = 999)

# 3. Create context
ctx <- context_create(model, n_ctx = 4096)

# 4. Generate text
messages <- list(
  list(role = "system", content = "You are a helpful assistant."),
  list(role = "user", content = "Write a haiku about coding.")
)

prompt <- apply_chat_template(model, messages)
response <- generate(ctx, prompt, max_tokens = 50, temperature = 0.7)

cat(response)
#> Lines of code flow
#> Logic builds like morning dew
#> Bugs hide, then we debug

Summary

Function Purpose
list_ollama_models() Discover available Ollama models
model_load("ollama:name") Load specific Ollama model
model_load("ollama") Interactive model selection

Next Steps

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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